1
|
Wei L, Liu P, Ren H, Xiao D. Research on helmet wearing detection method based on deep learning. Sci Rep 2024; 14:7010. [PMID: 38528034 DOI: 10.1038/s41598-024-57433-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 03/18/2024] [Indexed: 03/27/2024] Open
Abstract
The vigorous development of the construction industry has also brought unprecedented safety risks. The wearing of safety helmets at the construction site can effectively reduce casualties. As a result, this paper suggests employing a deep learning-based approach for the real-time detection of safety helmet usage among construction workers. Based on the selected YOLOv5s network through experiments, this paper analyzes its training results. Considering its poor detection effect on small objects and occluded objects. Therefore, multiple attention mechanisms are used to improve the YOLOv5s network, the feature pyramid network is improved into a BiFPN bidirectional feature pyramid network, and the post-processing method NMS is improved into Soft-NMS. Based on the above-improved method, the loss function is improved to enhance the convergence speed of the model and improve the detection speed. We propose a network model called BiFEL-YOLOv5s, which combines the BiFPN network and Focal-EIoU Loss to improve YOLOv5s. The average precision of the model is increased by 0.9% the recall rate is increased by 2.8%, and the detection speed of the model does not decrease too much. It is better suited for real-time safety helmet object detection, addressing the requirements of helmet detection across various work scenarios.
Collapse
Affiliation(s)
- Lihong Wei
- School of Artificial Intelligence and Big Data, Hulunbeier University, Inner Mongolia, 021008, Hailar, China
| | - Panpan Liu
- Information Science and Engineering School, Northeastern University, Shenyang, 110004, China.
| | - Haihui Ren
- Information Science and Engineering School, Northeastern University, Shenyang, 110004, China
| | - Dong Xiao
- Information Science and Engineering School, Northeastern University, Shenyang, 110004, China
- Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, Shenyang, 110819, China
| |
Collapse
|
2
|
Huang Y, Xiong J, Yao Z, Huang Q, Tang K, Jiang D, Yang Z. A fluorescence detection method for postharvest tomato epidermal defects based on improved YOLOv5m. J Sci Food Agric 2024. [PMID: 38523076 DOI: 10.1002/jsfa.13486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/19/2024] [Accepted: 03/22/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND Tomato quality visual grading is greatly affected by the problems of smooth skin, uneven illumination and invisible defects difficult to identify. The realization of intelligent detection of postharvest epidermal defects is conducive to further improving the economic value of postharvest tomatoes. RESULTS An image acquisition device that utilizes fluorescent technology has been designed to capture a dataset of tomato skin defects, encompassing categories such as rot defects, crack defects, and imperceptible defects. Then, the YOLOv5m model was improved by integrating Convolutional Block Attention Module and replacing part of the convolution kernels in the backbone network with Switchable Atrous Convolution. The results of comparison experiments and ablation experiments show that the Precision, Recall and mean Average Precision of the improved YOLOv5m model were 89.93 %, 82.33 % and 87.57 %, which higher than the YOLOv5m, Faster R-CNN and YOLOv7, and the average detection time was reduced by 47.04 ms/pic. CONCLUSION This article utilizes fluorescence imaging and an improved YOLOv5m model to detect tomato epidermal defects, resulting in better identification of imperceptible defects and detection of multiple categories of defects. This provides strong technical support for intelligent detection and quality grading of tomatoes. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Yuhua Huang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Juntao Xiong
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Zhaoshen Yao
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Qiyin Huang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Kun Tang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Dandan Jiang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| | - Zhengang Yang
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
| |
Collapse
|
3
|
Zhou T, Wang H, Du Y, Liu F, Guo Y, Lu H. M 3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection. Comput Biol Med 2024; 173:108291. [PMID: 38522254 DOI: 10.1016/j.compbiomed.2024.108291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/28/2024] [Accepted: 03/12/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. METHODS To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. RESULTS The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. CONCLUSION The M3YOLOv5 model can effectively recognize and locate the mandibular fracture region, which is of great significance for doctors' clinical diagnosis.
Collapse
Affiliation(s)
- Tao Zhou
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Hongwei Wang
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China.
| | - Yuhu Du
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Fengzhen Liu
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Yujie Guo
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Huiling Lu
- School of Medical Information and Engineering, Ningxia Medical University, Yinchuan, 750004, China.
| |
Collapse
|
4
|
Park J, Lee J, Jeong J. YOLOv5 based object detection in reel package X-ray images of semiconductor component. Heliyon 2024; 10:e26532. [PMID: 38434311 PMCID: PMC10907659 DOI: 10.1016/j.heliyon.2024.e26532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
The industrial manufacturing landscape is currently shifting toward the incorporation of technologies based on artificial intelligence (AI). This transition includes an evolution toward smart factory infrastructure, with a specific focus on AI-driven strategies in production and quality control. Specifically, AI-empowered computer vision has emerged as a potent tool that offers a departure from extant rule-based systems and provides enhanced operational efficiency at manufacturing sites. As the manufacturing sector embraces this new paradigm, the impetus to integrate AI-integrated manufacturing is evident. Within this framework, one salient application is AI deep learning-facilitated small-object detection, which is poised to have extensive implications for diverse industrial applications. This study describes an optimized iteration of the YOLOv5 model, which is known for its efficacious single-stage object-detection abilities underpinned by PyTorch. Our proposed "improved model" incorporates an additional layer to the model's canonical three-layer architecture, augmenting accuracy and computational expediency. Empirical evaluations using semiconductor X-ray imagery reveal the model's superior performance metrics. Given the intricate specifications of surface-mount technologies, which are characterized by a plethora of micro-scale components, our model makes a seminal contribution to real-time, in-line production assessments. Quantitative analyses show that our improved model attained a mean average precision of 0.622, surpassing YOLOv5's 0.349, and a marked accuracy enhancement of 0.865, which is a significant improvement on YOLOv5's 0.552. These findings bolster the model's robustness and potential applicability, particularly in discerning objects at reel granularities during real-time inferencing.
Collapse
Affiliation(s)
- Jinwoo Park
- Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Hygino AI Research Lab, 248-25 Simidaero, Dongan-gu, An-yang-si, Gyeonggi-do, 14067, Republic of Korea
| | - Jaehyeong Lee
- Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Jongpil Jeong
- Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea
| |
Collapse
|
5
|
Lai Q, Wang Y, Tan Y, Sun W. Design and experiment of Panax notoginseng root orientation transplanting device based on YOLOv5s. Front Plant Sci 2024; 15:1325420. [PMID: 38525144 PMCID: PMC10957537 DOI: 10.3389/fpls.2024.1325420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 02/21/2024] [Indexed: 03/26/2024]
Abstract
Consistent root orientation is one of the important requirements of Panax notoginseng transplanting agronomy. In this paper, a Panax notoginseng orientation transplanting method based on machine vision technology and negative pressure adsorption principle was proposed. With the cut-main root of Panax notoginseng roots as the detection object, the YOLOv5s was used to establish a root feature detection model. A Panax notoginseng root orientation transplanting device was designed. The orientation control system identifies the root posture according to the detection results and controls the orientation actuator to adjust the root posture. The detection results show that the precision rate of the model was 94.2%, the recall rate was 92.0%, and the average detection precision was 94.9%. The Box-Behnken experiments were performed to investigate the effects of suction plate rotation speed, servo rotation speed and the angle between the camera and the orientation actuator(ACOA) on the orientation qualification rate and root drop rate. Response surface method and objective optimisation algorithm were used to analyse the experimental results. The optimal working parameters were suction plate rotation speed of 5.73 r/min, servo rotation speed of 0.86 r/s and ACOA of 35°. Under this condition, the orientation qualification rate and root drop rate of the actual experiment were 89.87% and 6.57%, respectively, which met the requirements of orientation transplanting for Panax notoginseng roots. The research method of this paper is helpful to solve the problem of orientation transplanting of other root crops.
Collapse
Affiliation(s)
- Qinghui Lai
- School of Energy and Environment Science, Yunnan Provincial Rural Energy Engineering Key Laboratory, Yunnan Normal University, Kunming, China
| | - Yongjie Wang
- Faculty of Modern Agriculture Engineering, Kunming University of Science and Technology, Kunming, China
| | - Yu Tan
- Faculty of Modern Agriculture Engineering, Kunming University of Science and Technology, Kunming, China
| | - Wenqiang Sun
- Faculty of Modern Agriculture Engineering, Kunming University of Science and Technology, Kunming, China
| |
Collapse
|
6
|
Lian-Suo WEI, Shen-Hao H, Long-Yu M. MTD- YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model. Heliyon 2024; 10:e26145. [PMID: 38390090 PMCID: PMC10881355 DOI: 10.1016/j.heliyon.2024.e26145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 01/27/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
Underwater light attenuation leads to decreased image contrast. This reduction in contrast subsequently decreases target visibility. Additionally, marine target detection is challenging due to multi-scale problems from varying target-to-device distances, complex target clustering, and noise from waterborne particulates.To address these issues, we propose MTD-YOLOv5.Initially, we enhance image contrast with grayscale equalization and mitigate color shift issues through color space transformation.We then introduce a novel feature extraction module, PCBR, combining max pooling and convolution layers for more effective target feature extraction from the background.Furthermore, we present the Multi-Scale Perceptual Hybrid Pooling (MHP) module.This module integrates horizontal and vertical receptive fields to establish long-range dependencies, thereby capturing hidden target information in deep network feature maps. In the Labeled Fishes in the Wild test datasets, MTD-YOLOv5 achieves a precision of 88.1% and a mean Average Precision (mAP[0.5:.95]) of 49.6%.These results represent improvements of 2.6% in precision and 0.4% in mAP over the original YOLOv5.
Collapse
Affiliation(s)
- W E I Lian-Suo
- School of information engineering, Suqian University, SuQian, jiangsu 223800, China
| | - Huang Shen-Hao
- College of Computer & Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, China
| | - Ma Long-Yu
- College of Computer & Control Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, China
| |
Collapse
|
7
|
Jayagopal P, Purushothaman Janaki K, Mohan P, Kondapaneni UB, Periyasamy J, Mathivanan SK, Dalu GT. A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm. Sci Rep 2024; 14:4814. [PMID: 38413679 PMCID: PMC10899584 DOI: 10.1038/s41598-024-54399-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543 ms and computational time of 5.987 ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP.
Collapse
Affiliation(s)
- Prabhu Jayagopal
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India
| | - Kumar Purushothaman Janaki
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India
| | - Prakash Mohan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | - Upendra Babu Kondapaneni
- School of Computing, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Bharath Institute of Science and Technology, 173, Agaram Main Road, Selaiyur, Tambaram, Chennai, 600073, Tamil Nadu, India
| | - Jayalakshmi Periyasamy
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India
| | | | - Gemmachis Teshite Dalu
- Department of Software Engineering, College of Computing and Informatics, Haramaya University, POB 138, Dire Dawa, Ethiopia.
| |
Collapse
|
8
|
Pereira T, Gameiro T, Pedro J, Viegas C, Ferreira NMF. Vision System for a Forestry Navigation Machine. Sensors (Basel) 2024; 24:1475. [PMID: 38475010 DOI: 10.3390/s24051475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system's pivotal contribution to the autonomous navigation of robots in forest environments.
Collapse
Affiliation(s)
- Tiago Pereira
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
| | - Tiago Gameiro
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
| | - José Pedro
- ADAI (Associação para o Desenvolvimento da Aerodinâmica Industrial), Department of Mechanical Engineering, University of Coimbra, Rua Luís Reis Santos, Pólo II, 3030-788 Coimbra, Portugal
| | - Carlos Viegas
- ADAI (Associação para o Desenvolvimento da Aerodinâmica Industrial), Department of Mechanical Engineering, University of Coimbra, Rua Luís Reis Santos, Pólo II, 3030-788 Coimbra, Portugal
| | - N M Fonseca Ferreira
- Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
- GECAD-Knowledge Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Engineering Institute of Porto (ISEP), Polytechnic Institute of Porto (IPP), 4200-465 Porto, Portugal
| |
Collapse
|
9
|
Tassoker M, Öziç MÜ, Yuce F. Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs. Sci Rep 2024; 14:4437. [PMID: 38396289 PMCID: PMC10891049 DOI: 10.1038/s41598-024-55109-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 02/20/2024] [Indexed: 02/25/2024] Open
Abstract
Idiopathic osteosclerosis (IO) are focal radiopacities of unknown etiology observed in the jaws. These radiopacities are incidentally detected on dental panoramic radiographs taken for other reasons. In this study, we investigated the performance of a deep learning model in detecting IO using a small dataset of dental panoramic radiographs with varying contrasts and features. Two radiologists collected 175 IO-diagnosed dental panoramic radiographs from the dental school database. The dataset size is limited due to the rarity of IO, with its incidence in the Turkish population reported as 2.7% in studies. To overcome this limitation, data augmentation was performed by horizontally flipping the images, resulting in an augmented dataset of 350 panoramic radiographs. The images were annotated by two radiologists and divided into approximately 70% for training (245 radiographs), 15% for validation (53 radiographs), and 15% for testing (52 radiographs). The study employing the YOLOv5 deep learning model evaluated the results using precision, recall, F1-score, mAP (mean Average Precision), and average inference time score metrics. The training and testing processes were conducted on the Google Colab Pro virtual machine. The test process's performance criteria were obtained with a precision value of 0.981, a recall value of 0.929, an F1-score value of 0.954, and an average inference time of 25.4 ms. Although radiographs diagnosed with IO have a small dataset and exhibit different contrasts and features, it has been observed that the deep learning model provides high detection speed, accuracy, and localization results. The automatic identification of IO lesions using artificial intelligence algorithms, with high success rates, can contribute to the clinical workflow of dentists by preventing unnecessary biopsy procedure.
Collapse
Affiliation(s)
- Melek Tassoker
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Necmettin Erbakan University, Bağlarbaşı Street, 42090, Meram, Konya, Turkey.
| | - Muhammet Üsame Öziç
- Faculty of Technology, Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey
| | - Fatma Yuce
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Istanbul Okan University, Istanbul, Turkey
| |
Collapse
|
10
|
Xie X, Qin Y, Zhang Z, Yan Z, Jin H, Xu M, Zhang C. GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots. Sensors (Basel) 2024; 24:1374. [PMID: 38474909 DOI: 10.3390/s24051374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/07/2024] [Accepted: 02/10/2024] [Indexed: 03/14/2024]
Abstract
Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While mAP@0.5 increased by 0.514% to 95.364, the model simultaneously reduced the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system localization accuracy and robustness. It shows a remarkable 92.59% improvement in RMSE for Absolute Trajectory Error (ATE), along with a 93.11% improvement in RMSE for the translational drift of Relative Pose Error (RPE) and a 92.89% improvement in RMSE for the rotational drift of RPE. Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.
Collapse
Affiliation(s)
- Xiaolin Xie
- Longmen Laboratory, Luoyang 471003, China
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Yibo Qin
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Zhihong Zhang
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Zixiang Yan
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Hang Jin
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Man Xu
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Cheng Zhang
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| |
Collapse
|
11
|
Suhail K, Brindha D. Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization. Comput Biol Med 2024; 169:107895. [PMID: 38183704 DOI: 10.1016/j.compbiomed.2023.107895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/07/2023] [Accepted: 12/22/2023] [Indexed: 01/08/2024]
Abstract
The diagnosis of kidney disease often involves analysing urine sediment particles. Traditionally, urinalysis was performed manually by collecting urine samples and using a centrifuge, which was prone to manual errors and relied on labour-intensive processes. Automated urine sediment microscopy, based on machine learning models, requires segmentation and feature extraction, which can hinder model performance due to intrinsic characteristics of microscopic images. Deep learning models based on convolutional neural networks (CNNs) often rely on a large number of manually annotated data, making the system computationally complex. This study propose an advanced deep learning model based on YOLOv5, which offers faster performance and requires comparatively less data. The proposed model used five variants of the YOLOv5 model (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) to detect six categories of urine particles (erythrocyte, leukocyte, crystals, cast, mycete, epithelial cells) from microscopic urine sediment images. The dataset involved 5376 images of urine sediments with 6 particles. There are 30 sets of hyperparamreteres are employed in the YOLOv5 model. To optimize the hyperparameters and fine-tune with the urine sediment dataset and for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among the six categories of detected particles mycete achieved maximum performance with a mAP of 97.6 % and crystals achieved minimum performance with a mAP of 81.7 % with YOLOv5x model compared to other particles. To optimize the hyperparameters for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among all the models, YOLOv5l and YOLOv5x performed the best. YOLOv5l achieved a mean average precision (mAP) of 85.8 % while YOLOv5x achieved a mAP of 85.4 % at an IoU threshold of 0.5. The detection speed per image was 23.4 ms for YOLOv5l and 28.4 ms for YOLOv5x. The proposed method developed a faster and better automated microscopic model using advanced deep learning techniques to detect urinary particles from microscopic urine sediment images for kidney disease identification. The method demonstrated strong performance in urinalysis.
Collapse
Affiliation(s)
- K Suhail
- Department of Biomedical Engineering, PSG College of Technology, Coimbatore, 641004, India.
| | - D Brindha
- Department of Biomedical Engineering, PSG College of Technology, Coimbatore, 641004, India.
| |
Collapse
|
12
|
Wang S, Hao X. YOLO-SK: A lightweight multiscale object detection algorithm. Heliyon 2024; 10:e24143. [PMID: 38293400 PMCID: PMC10826665 DOI: 10.1016/j.heliyon.2024.e24143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 12/07/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024] Open
Abstract
YOLOv5 is an excellent object-detection model. However, it fails to fully use multiscale information when detecting objects with significant scale variations. It might use irrelevant contextual information, leading to incorrect predictions, particularly for low-performance devices. In this study, we selected lightweight YOLOv5s as the baseline model and proposed an improved model called YOLO-SK to overcome this limitation. YOLO-SK introduced several key improvements, the most important being the collaborative work of the weighted dense feature fusion network and SK attention prediction head. The proposed weighted dense feature fusion network could dynamically fuse features at different scales using autonomous learning parameters and cross-layer fusion capabilities. This enabled a balanced feature fusion ability in the output feature maps of different scales, thereby enhancing the richness of the effective information in the fused feature maps. The prediction head equipped with the SK attention mechanism broadened the scope of the model's receptive field and sharpened the focus on the target characteristics. This made it possible to glean more information about the target from the feature map output by employing a weighted dense feature fusion network. In addition, in order to improve the model's performance in terms of both accuracy and volume, we implemented the SIoU loss function and the Ghost Conv. The use of the model allowed for a more precise and in-depth comprehension of the event, which was made possible by all of these various methods of improvement. Extensive testing done on the PASCAL VOC 2007 and 2012 datasets showed that YOLO-SK was able to achieve considerable gains in prediction accuracy when compared with the baseline model (YOLOv5s), all while keeping the same level of model complexity. To be more specific, mAP@.5 increased by 2.6 %, and mAP@.5:.95 increased by 4.8 %. The advancements that were made and detailed in this paper could serve as a springboard for additional research that aims to improve the precision of multiscale object identification models for low-performance devices.
Collapse
Affiliation(s)
- Shihang Wang
- College of Computer Science and Technology (College of Big Data), Taiyuan University of Technology, Jinzhong 030600, China
| | - Xiaoli Hao
- College of Computer Science and Technology (College of Big Data), Taiyuan University of Technology, Jinzhong 030600, China
| |
Collapse
|
13
|
Luo H, Wei J, Wang Y, Chen J, Li W. An improved lightweight object detection algorithm for YOLOv5. PeerJ Comput Sci 2024; 10:e1830. [PMID: 38435620 PMCID: PMC10909222 DOI: 10.7717/peerj-cs.1830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/29/2023] [Indexed: 03/05/2024]
Abstract
Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model's inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model's ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling-Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.
Collapse
Affiliation(s)
- Hao Luo
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Jiangshu Wei
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Jinrong Chen
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Wujie Li
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| |
Collapse
|
14
|
Dai N, Lu Z, Chen J, Xu K, Hu X, Yuan Y. Research on the Algorithm of Position Correction for High-Speed Moving Express Packages Based on Traditional Vision and AI Vision. Sensors (Basel) 2024; 24:892. [PMID: 38339609 PMCID: PMC10857253 DOI: 10.3390/s24030892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
The rapid development of the logistics industry poses significant challenges to the sorting work within this sector. The fast and precise identification of moving express parcels holds immense significance for the performance of logistics sorting systems. This paper proposes a motion express parcel positioning algorithm that combines traditional vision and AI-based vision. In the traditional vision aspect, we employ a brightness-based traditional visual parcel detection algorithm. In the AI vision aspect, we introduce a Convolutional Block Attention Module (CBAM) and Focal-EIoU to enhance YOLOv5, improving the model's recall rate and robustness. Additionally, we adopt an Optimal Transport Assignment (OTA) label assignment strategy to provide a training dataset based on global optimality for the model training phase. Our experimental results demonstrate that our modified AI model surpasses traditional algorithms in both parcel recognition accuracy and inference speed. The combined approach of traditional vision and AI vision in the motion express parcel positioning algorithm proves applicable for practical logistics sorting systems.
Collapse
Affiliation(s)
- Ning Dai
- Key Laboratory of Modern Textile Machinery & Technology of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | | | | | | | | | | |
Collapse
|
15
|
Lv M, Su WH. YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection. Front Plant Sci 2024; 14:1323301. [PMID: 38288410 PMCID: PMC10822903 DOI: 10.3389/fpls.2023.1323301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/26/2023] [Indexed: 01/31/2024]
Abstract
Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.
Collapse
Affiliation(s)
| | - Wen-Hao Su
- College of Engineering, China Agricultural University, Beijing, China
| |
Collapse
|
16
|
Touko Mbouembe PL, Liu G, Park S, Kim JH. Accurate and fast detection of tomatoes based on improved YOLOv5s in natural environments. Front Plant Sci 2024; 14:1292766. [PMID: 38273960 PMCID: PMC10808679 DOI: 10.3389/fpls.2023.1292766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024]
Abstract
Uneven illumination, obstruction of leaves or branches, and the overlapping of fruit significantly affect the accuracy of tomato detection by automated harvesting robots in natural environments. In this study, a proficient and accurate algorithm for tomato detection, called SBCS-YOLOv5s, is proposed to address this practical challenge. SBCS-YOLOv5s integrates the SE, BiFPN, CARAFE and Soft-NMS modules into YOLOv5s to enhance the feature expression ability of the model. First, the SE attention module and the C3 module were combined to form the C3SE module, replacing the original C3 module within the YOLOv5s backbone architecture. The SE attention module relies on modeling channel-wise relationships and adaptive re-calibration of feature maps to capture important information, which helps improve feature extraction of the model. Moreover, the SE module's ability to adaptively re-calibrate features can improve the model's robustness to variations in environmental conditions. Next, the conventional PANet multi-scale feature fusion network was replaced with an efficient, weighted Bi-directional Feature Pyramid Network (BiFPN). This adaptation aids the model in determining useful weights for the comprehensive fusion of high-level and bottom-level features. Third, the regular up-sampling operator is replaced by the Content Aware Reassembly of Features (CARAFE) within the neck network. This implementation produces a better feature map that encompasses greater semantic information. In addition, CARAFE's ability to enhance spatial detail helps the model discriminate between closely spaced fruits, especially for tomatoes that overlap heavily, potentially reducing the number of merging detections. Finally, for heightened identification of occluded and overlapped fruits, the conventional Non-Maximum-Suppression (NMS) algorithm was substituted with the Soft-NMS algorithm. Since Soft-NMS adopts a continuous weighting scheme, it is more adaptable to varying object sizes, improving the handling of small or large fruits in the image. Remarkably, this is carried out without introducing changes to the computational complexity. The outcome of the experiments showed that SBCS-YOLOv5s achieved a mean average precision (mAP (0.5:0.95)) of 87.7%, which is 3.5% superior to the original YOLOv5s model. Moreover, SBCS-YOLOv5s has a detection speed of 2.6 ms per image. Compared to other state-of-the-art detection algorithms, SBCS-YOLOv5s performed the best, showing tremendous promise for tomato detection in natural environments.
Collapse
Affiliation(s)
| | - Guoxu Liu
- School of Computer Engineering, Weifang University, Weifang, China
- R&D Center, Univalsoft Joint Stock Co., Ltd., Shouguang, China
| | - Sungkyung Park
- Department of Electronics Engineering, Pusan National University, Busan, Republic of Korea
| | - Jae Ho Kim
- Department of Electronics Engineering, Pusan National University, Busan, Republic of Korea
- Exsolit Research Center, Yangsan, Republic of Korea
| |
Collapse
|
17
|
Kim JK, Chang MC, Park WT, Lee GW. Identification of L5 vertebra on lumbar spine radiographs using deep learning. J Int Med Res 2024; 52:3000605231223881. [PMID: 38206194 PMCID: PMC10785730 DOI: 10.1177/03000605231223881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024] Open
Abstract
OBJECTIVE Deep learning is an advanced machine-learning approach that is used in several medical fields. Here, we developed a deep learning model using an object detection algorithm to identify the L5 vertebra on anteroposterior lumbar spine radiographs, and assessed its detection accuracy. METHODS We retrospectively recruited 150 participants for whom both anteroposterior whole-spine and lumbar spine radiographs were available. The anteroposterior lumbar spine radiographs of these patients were used as the input data. Of the 150 images, 105 (70%) were randomly selected as the training set, and the remaining 45 (30%) were assigned to the validation set. YOLOv5x, of the YOLOv5 family model, was used to detect the L5 vertebra area. RESULTS The mean average precisions 0.5 and 0.75 of the trained L5 detection model were 99.2% and 96.9%, respectively. The model's precision was 95.7% and its recall was 97.8%. Furthermore, 93.3% of the validation data were correctly detected. CONCLUSION Our deep learning model showed an outstanding ability to identify L5 vertebrae.
Collapse
Affiliation(s)
- Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, Yeungnam University College of Medicine, Daegu, Republic of Korea
| | - Wook Tae Park
- Department of Orthopaedic Surgery, Yeungnam University College of Medicine, Daegu, Republic of Korea
| | - Gun Woo Lee
- Department of Orthopaedic Surgery, Yeungnam University College of Medicine, Daegu, Republic of Korea
| |
Collapse
|
18
|
Gkouzionis I, Zhong Y, Nazarian S, Darzi A, Patel N, Peters CJ, Elson DS. A YOLOv5-based network for the detection of a diffuse reflectance spectroscopy probe to aid surgical guidance in gastrointestinal cancer surgery. Int J Comput Assist Radiol Surg 2024; 19:11-14. [PMID: 37289279 PMCID: PMC10769906 DOI: 10.1007/s11548-023-02944-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/28/2023] [Indexed: 06/09/2023]
Abstract
PURPOSE A positive circumferential resection margin (CRM) for oesophageal and gastric carcinoma is associated with local recurrence and poorer long-term survival. Diffuse reflectance spectroscopy (DRS) is a non-invasive technology able to distinguish tissue type based on spectral data. The aim of this study was to develop a deep learning-based method for DRS probe detection and tracking to aid classification of tumour and non-tumour gastrointestinal (GI) tissue in real time. METHODS Data collected from both ex vivo human tissue specimen and sold tissue phantoms were used for the training and retrospective validation of the developed neural network framework. Specifically, a neural network based on the You Only Look Once (YOLO) v5 network was developed to accurately detect and track the tip of the DRS probe on video data acquired during an ex vivo clinical study. RESULTS Different metrics were used to analyse the performance of the proposed probe detection and tracking framework, such as precision, recall, mAP 0.5, and Euclidean distance. Overall, the developed framework achieved a 93% precision at 23 FPS for probe detection, while the average Euclidean distance error was 4.90 pixels. CONCLUSION The use of a deep learning approach for markerless DRS probe detection and tracking system could pave the way for real-time classification of GI tissue to aid margin assessment in cancer resection surgery and has potential to be applied in routine surgical practice.
Collapse
Affiliation(s)
- Ioannis Gkouzionis
- Hamlyn Center, Imperial College London, London, UK.
- Department of Surgery and Cancer, Imperial College London, London, UK.
| | - Yican Zhong
- Hamlyn Center, Imperial College London, London, UK
| | - Scarlet Nazarian
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Ara Darzi
- Hamlyn Center, Imperial College London, London, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Nisha Patel
- Department of Surgery and Cancer, Imperial College London, London, UK
| | | | - Daniel S Elson
- Hamlyn Center, Imperial College London, London, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
| |
Collapse
|
19
|
Huang X, Zhang Y. ScanGuard-YOLO: Enhancing X-ray Prohibited Item Detection with Significant Performance Gains. Sensors (Basel) 2023; 24:102. [PMID: 38202964 PMCID: PMC10780801 DOI: 10.3390/s24010102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/11/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024]
Abstract
To address the problem of low recall rate in the detection of prohibited items in X-ray images due to the severe object occlusion and complex background, an X-ray prohibited item detection network, ScanGuard-YOLO, based on the YOLOv5 architecture, is proposed to effectively improve the model's recall rate and the comprehensive metric F1 score. Firstly, the RFB-s module was added to the end part of the backbone, and dilated convolution was used to increase the receptive field of the backbone network to better capture global features. In the neck section, the efficient RepGFPN module was employed to fuse multiscale information from the backbone output. This aimed to capture details and contextual information at various scales, thereby enhancing the model's understanding and representation capability of the object. Secondly, a novel detection head was introduced to unify scale-awareness, spatial-awareness, and task-awareness altogether, which significantly improved the representation ability of the object detection heads. Finally, the bounding box regression loss function was defined as the WIOUv3 loss, effectively balancing the contribution of low-quality and high-quality samples to the loss. ScanGuard-YOLO was tested on OPIXray and HiXray datasets, showing significant improvements compared to the baseline model. The mean average precision (mAP@0.5) increased by 2.3% and 1.6%, the recall rate improved by 4.5% and 2%, and the F1 score increased by 2.3% and 1%, respectively. The experimental results demonstrate that ScanGuard-YOLO effectively enhances the detection capability of prohibited items in complex backgrounds and exhibits broad prospects for application.
Collapse
Affiliation(s)
- Xianning Huang
- School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
| | - Yaping Zhang
- School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
| |
Collapse
|
20
|
Gao R, Ma Y, Zhao Z, Li B, Zhang J. Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention. Sensors (Basel) 2023; 23:9861. [PMID: 38139707 PMCID: PMC10747497 DOI: 10.3390/s23249861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/24/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
Currently, aeroplane images captured by camera sensors are characterized by their small size and intricate backgrounds, posing a challenge for existing deep learning algorithms in effectively detecting small targets. This paper incorporates the RFBNet (a coordinate attention mechanism) and the SIOU loss function into the YOLOv5 algorithm to address this issue. The result is developing the model for aeroplane and undercarriage detection. The primary goal is to synergize camera sensors with deep learning algorithms, improving image capture precision. YOLOv5-RSC enhances three aspects: firstly, it introduces the receptive field block based on the backbone network, increasing the size of the receptive field of the feature map, enhancing the connection between shallow and deep feature maps, and further improving the model's utilization of feature information. Secondly, the coordinate attention mechanism is added to the feature fusion network to assist the model in more accurately locating the targets of interest, considering attention in the channel and spatial dimensions. This enhances the model's attention to key information and improves detection precision. Finally, the SIoU bounding box loss function is adopted to address the issue of IoU's insensitivity to scale and increase the speed of model bounding box convergence. Subsequently, the Basler camera experimental platform was constructed for experimental verification. The results demonstrate that the AP values of the YOLOv5-RSC detection model for aeroplane and undercarriage are 92.4% and 80.5%, respectively. The mAP value is 86.4%, which is 2.0%, 5.4%, and 3.7% higher than the original YOLOv5 algorithm, respectively, with a detection speed reaching 89.2 FPS. These findings indicate that the model exhibits high detection precision and speed, providing a valuable reference for aeroplane undercarriage detection.
Collapse
Affiliation(s)
- Ruizhen Gao
- School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China
- Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China
- Collaborative Innovation Center for Modern Equipment Manufacturing of Jinan New Area (Hebei), Handan 056038, China
| | - Ya’nan Ma
- School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China
| | - Ziyue Zhao
- School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China
| | - Baihua Li
- Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK
| | - Jingjun Zhang
- School of Mechanical Engineering and Equipment, Hebei University of Engineering, Handan 056038, China
- Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China
- Collaborative Innovation Center for Modern Equipment Manufacturing of Jinan New Area (Hebei), Handan 056038, China
| |
Collapse
|
21
|
Ficzere M, Péterfi O, Farkas A, Nagy ZK, Galata DL. Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision. Eur J Pharm Sci 2023; 191:106611. [PMID: 37844806 DOI: 10.1016/j.ejps.2023.106611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/21/2023] [Accepted: 10/14/2023] [Indexed: 10/18/2023]
Abstract
This work presents a system, where deep learning was used on images captured with a digital camera to simultaneously determine the API concentration and the particle size distribution (PSD) of two components of a powder blend. The blend consisted of acetylsalicylic acid (ASA) and calcium hydrogen phosphate (CHP), and the predicted API concentration was found corresponding with the HPLC measurements. The PSDs determined with the method corresponded with those measured with laser diffraction particle size analysis. This novel method provides fast and simple measurements and could be suitable for detecting segregation in the powder. By examining the powders discharged from a batch blender, the API concentrations at the top and bottom of the container could be measured, yielding information about the adequacy of the blending and improving the quality control of the manufacturing process.
Collapse
Affiliation(s)
- Máté Ficzere
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., Budapest H 1111, Hungary
| | - Orsolya Péterfi
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., Budapest H 1111, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., Budapest H 1111, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., Budapest H 1111, Hungary.
| | - Dorián László Galata
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp 3., Budapest H 1111, Hungary
| |
Collapse
|
22
|
Salguero JSL, Rendón MR, Valencia JT, Gil JAC, Galvis CAN, Londoño OM, Calderón CLL, Osorio FAG, Soto RT. Automatic detection of Cryptosporidium in optical microscopy images using YOLOv5x: a comparative study. Biochem Cell Biol 2023; 101:538-549. [PMID: 37586108 DOI: 10.1139/bcb-2023-0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023] Open
Abstract
Here, a machine learning tool (YOLOv5) enables the detection of Cryptosporidium microorganisms using optical and phase contrast microscope images. The two databases were processed using 520 images (optical microscopy) and 1200 images (phase contrast microscopy). It used Python libraries to label, standardize the size, and crop the images to generate the input tensors to the YOLOv5 network (s, m, and l). It implemented two experiments using randomly initialized weights in optical and phase contrast microscope images. The other two experiments used the parameters for the best training time obtained before and after retraining the models. Metrics used to assess model accuracy were mean average accuracy, confusion matrix, and the F1 scores. All three metrics confirmed that the optimal model used the best epoch of optical imaging training and retraining with phase contrast imaging. Experiments with randomly initialized weights with optical imaging showed the lowest precision for Cryptosporidium detection. The most stable model was YOLOv5m, with the best results in all categories. However, the differences between all models are lower than 2%, and YOLOv5s is the best option for Cryptosporidium detection considering the differences in computational costs of the models.
Collapse
Affiliation(s)
- Johan Sebastian Lopez Salguero
- Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Melissa Rodríguez Rendón
- Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Jessica Triviño Valencia
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Jorge Andrés Cuellar Gil
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Carlos Andrés Naranjo Galvis
- Departamento de Ciencias Básicas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - Oscar Moscoso Londoño
- Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | - César Leandro Londoño Calderón
- Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
| | | | - Reinel Tabares Soto
- Departamento de Electrónica y Automatización Industrial, Universidad Autónoma de Manizales, Antigua Estación del Ferrocarril, Manizales, CP 170001, Colombia
- Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, CP 170001, Caldas, Colombia
| |
Collapse
|
23
|
Liu J, Wang X, Zhu Q, Miao W. Tomato brown rot disease detection using improved YOLOv5 with attention mechanism. Front Plant Sci 2023; 14:1289464. [PMID: 38053763 PMCID: PMC10694285 DOI: 10.3389/fpls.2023.1289464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/30/2023] [Indexed: 12/07/2023]
Abstract
Brown rot disease poses a severe threat to tomato plants, resulting in reduced yields. Therefore, the accurate and efficient detection of tomato brown rot disease through deep learning technology holds immense importance for enhancing productivity. However, intelligent disease detection in complex scenarios remains a formidable challenge. Current object detection methods often fall short in practical applications and struggle to capture features from small objects. To overcome these limitations, we present an enhanced algorithm in this study, building upon YOLOv5s with an integrated attention mechanism for tomato brown rot detection. We introduce a hybrid attention module into the feature prediction structure of YOLOv5s to improve the model's ability to discern tomato brown rot objects in complex contexts. Additionally, we employ the CIOU loss function for precise border regression. Our experiments are conducted using a custom tomato disease dataset, and the results demonstrate the superiority of our enhanced algorithm over other models. It achieves an impressive average accuracy rate of 94.6% while maintaining a rapid detection speed of 112 frames per second. This innovation marks a significant step toward robust and efficient disease detection in tomato plants.
Collapse
Affiliation(s)
- Jun Liu
- Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China
| | - Xuewei Wang
- Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China
| | | | | |
Collapse
|
24
|
Qiu S, Li Y, Gao J, Li X, Yuan X, Liu Z, Cui Q, Wu C. Research and Implementation of Millet Ear Detection Method Based on Lightweight YOLOv5. Sensors (Basel) 2023; 23:9189. [PMID: 38005575 PMCID: PMC10675272 DOI: 10.3390/s23229189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/08/2023] [Accepted: 11/12/2023] [Indexed: 11/26/2023]
Abstract
As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deploy the real-time detection of millet ears on mobile devices. A lightweight real-time detection method for millet ears is based on YOLOv5. First, the YOLOv5s model is improved by replacing the YOLOv5s backbone feature extraction network with the MobilenetV3 lightweight model to reduce model size. Then, using the multi-feature fusion detection structure, the micro-scale detection layer is augmented to reduce high-level feature maps and low-level feature maps. The Merge-NMS technique is used in post-processing for target information loss to reduce the influence of boundary blur on the detection effect and increase the detection accuracy of small and obstructed targets. Finally, the models reconstructed by different improved methods are trained and tested on the self-built millet ear data set. The AP value of the improved model in this study reaches 97.78%, F1-score is 94.20%, and the model size is only 7.56 MB, which is 53.28% of the standard YoloV5s model size, and has a better detection speed. Compared with other classical target detection models, it shows strong robustness and generalization ability. The lightweight model performs better in the detection of pictures and videos in the Jetson Nano. The results show that the improved lightweight YOLOv5 millet detection model in this study can overcome the influence of complex environments, and significantly improve the detection effect of millet under dense distribution and occlusion conditions. The millet detection model is deployed on the Jetson Nano, and the millet detection system is implemented based on the PyQt5 framework. The detection accuracy and detection speed of the millet detection system can meet the actual needs of intelligent agricultural machinery equipment and has a good application prospect.
Collapse
Affiliation(s)
- Shujin Qiu
- College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China; (Y.L.); (J.G.); (X.L.); (Z.L.); (Q.C.); (C.W.)
| | - Yun Li
- College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China; (Y.L.); (J.G.); (X.L.); (Z.L.); (Q.C.); (C.W.)
| | - Jian Gao
- College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China; (Y.L.); (J.G.); (X.L.); (Z.L.); (Q.C.); (C.W.)
| | - Xiaobin Li
- College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China; (Y.L.); (J.G.); (X.L.); (Z.L.); (Q.C.); (C.W.)
| | - Xiangyang Yuan
- College of Agricultural, Shanxi Agricultural University, Jinzhong 030801, China;
| | - Zhenyu Liu
- College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China; (Y.L.); (J.G.); (X.L.); (Z.L.); (Q.C.); (C.W.)
| | - Qingliang Cui
- College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China; (Y.L.); (J.G.); (X.L.); (Z.L.); (Q.C.); (C.W.)
| | - Cuiqing Wu
- College of Agricultural Engineering, Shanxi Agriculture University, Jinzhong 030801, China; (Y.L.); (J.G.); (X.L.); (Z.L.); (Q.C.); (C.W.)
| |
Collapse
|
25
|
Zou Y, Tian Z, Cao J, Ren Y, Zhang Y, Liu L, Zhang P, Ni J. Rice Grain Detection and Counting Method Based on TCLE-YOLO Model. Sensors (Basel) 2023; 23:9129. [PMID: 38005517 PMCID: PMC10675024 DOI: 10.3390/s23229129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/06/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023]
Abstract
Thousand-grain weight is the main parameter for accurately estimating rice yields, and it is an important indicator for variety breeding and cultivation management. The accurate detection and counting of rice grains is an important prerequisite for thousand-grain weight measurements. However, because rice grains are small targets with high overall similarity and different degrees of adhesion, there are still considerable challenges preventing the accurate detection and counting of rice grains during thousand-grain weight measurements. A deep learning model based on a transformer encoder and coordinate attention module was, therefore, designed for detecting and counting rice grains, and named TCLE-YOLO in which YOLOv5 was used as the backbone network. Specifically, to improve the feature representation of the model for small target regions, a coordinate attention (CA) module was introduced into the backbone module of YOLOv5. In addition, another detection head for small targets was designed based on a low-level, high-resolution feature map, and the transformer encoder was applied to the neck module to expand the receptive field of the network and enhance the extraction of key feature of detected targets. This enabled our additional detection head to be more sensitive to rice grains, especially heavily adhesive grains. Finally, EIoU loss was used to further improve accuracy. The experimental results show that, when applied to the self-built rice grain dataset, the precision, recall, and mAP@0.5 of the TCLE-YOLO model were 99.20%, 99.10%, and 99.20%, respectively. Compared with several state-of-the-art models, the proposed TCLE-YOLO model achieves better detection performance. In summary, the rice grain detection method built in this study is suitable for rice grain recognition and counting, and it can provide guidance for accurate thousand-grain weight measurements and the effective evaluation of rice breeding.
Collapse
Affiliation(s)
- Yu Zou
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China;
| | - Zefeng Tian
- College of Engineering, Anhui Agricultural University, Hefei 230036, China; (Z.T.); (J.C.)
| | - Jiawen Cao
- College of Engineering, Anhui Agricultural University, Hefei 230036, China; (Z.T.); (J.C.)
| | - Yi Ren
- College of Agriculture, Anhui Science and Technology University, Chuzhou 239000, China;
| | - Yaping Zhang
- Hefei Institute of Technology Innovation Engineering, Chinese Academy of Sciences, Hefei 230094, China; (Y.Z.); (L.L.)
| | - Lu Liu
- Hefei Institute of Technology Innovation Engineering, Chinese Academy of Sciences, Hefei 230094, China; (Y.Z.); (L.L.)
| | - Peijiang Zhang
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China;
| | - Jinlong Ni
- Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China;
| |
Collapse
|
26
|
Kan X, Zhu S, Zhang Y, Qian C. A Lightweight Human Fall Detection Network. Sensors (Basel) 2023; 23:9069. [PMID: 38005456 PMCID: PMC10674212 DOI: 10.3390/s23229069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/26/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023]
Abstract
The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techniques for human fall detection. However, this algorithm, although advanced, grapples with issues such as computational demands, challenges in hardware integration, and vulnerability to occlusions in the designated target group. To address these limitations, we introduce a pioneering lightweight approach named CGNS-YOLO for human fall detection. Our method incorporates both the GSConv module and the GDCN module to reconfigure the neck network of YOLOv5s. The objective behind this modification is to diminish the model size, curtail floating-point computations during feature channel fusion, and bolster feature extraction efficacy, thereby enhancing hardware adaptability. We also integrate a normalization-based attention module (NAM) into the framework, which concentrates on salient fall-related data and deemphasizes less pertinent information. This strategic refinement augments the algorithm's precision. By embedding the SCYLLA Intersection over Union (SIoU) loss function, our model benefits from faster convergence and heightened detection precision. We evaluated our model using the Multicam dataset and the Le2i Fall Detection dataset. Our findings indicate a 1.2% enhancement in detection accuracy compared with the conventional YOLOv5s framework. Notably, our model realized a 20.3% decrease in parameter tally and a 29.6% drop in floating-point operations. A comprehensive instance analysis and comparative assessments underscore the method's superiority and efficacy.
Collapse
Affiliation(s)
- Xi Kan
- School of the Internet of Things Engineering, Wuxi University, Wuxi 214105, China; (X.K.); (C.Q.)
| | - Shenghao Zhu
- School of Automation, Nanjing University of Information Science & Technology, Nanjing 211800, China;
| | - Yonghong Zhang
- School of the Internet of Things Engineering, Wuxi University, Wuxi 214105, China; (X.K.); (C.Q.)
- School of Automation, Nanjing University of Information Science & Technology, Nanjing 211800, China;
| | - Chengshan Qian
- School of the Internet of Things Engineering, Wuxi University, Wuxi 214105, China; (X.K.); (C.Q.)
- School of Automation, Nanjing University of Information Science & Technology, Nanjing 211800, China;
| |
Collapse
|
27
|
Kalbhor M, Shinde S, Wajire P, Jude H. CerviCell-detector: An object detection approach for identifying the cancerous cells in pap smear images of cervical cancer. Heliyon 2023; 9:e22324. [PMID: 38058644 PMCID: PMC10696000 DOI: 10.1016/j.heliyon.2023.e22324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 10/18/2023] [Accepted: 11/09/2023] [Indexed: 12/08/2023] Open
Abstract
Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening for cervical cancer is the pap smear test. Pap smear is a time-consuming test as it requires detailed analysis by expert cytologists. Cytologists can screen around 100 to 1000 slides depending upon the availability of advanced equipment. It requires substantial time and effort to carefully examine each slide, identify and classify cells, and make accurate diagnoses. Prolonged periods of visual inspection can increase the likelihood of human errors, such as overlooking abnormalities or misclassifying cells. The sheer volume of slides to be screened can exacerbate fatigue and impact diagnostic accuracy. Due to this reason Artificial intelligence (AI) based computer-aided diagnosis system for the classification and detection of pap smear images is needed. There are some AI-based solutions proposed in the literature, still, an effective and accurate system is under research. In this paper, we implement a state-of-the-art object detection model with a newly available CRIC dataset which follows the Bethesda system for nomenclature. Object detection models implemented are YOLOv5 which uses the CSPNet backbone, Faster R-CNN which has Region Proposal Network (RPN) and Detectron2 framework created by Facebook AI Research (FAIR) Group. ResNext model is implemented among the available models from Detectron2. The CRIC dataset is preprocessed and augmented using Roboflow tool. The performance measures of Average Precision and mean Average precision over the Intersection over Union (IoU) are used to evaluate the effectiveness of the models. The models performed better for two classes namely Normal and Abnormal compared to six classes from the Bethesda system. The highest mean Average Precision (mAP) is observed on the augmented dataset for YOLOv5 models for binary classification with 83 % mAP with IoU in the range of 0.50-0.95.
Collapse
Affiliation(s)
| | - Swati Shinde
- Pimpri Chinchwad College of Engineering, Pune, India
| | - Pankaj Wajire
- Pimpri Chinchwad College of Engineering, Pune, India
| | - Hemanth Jude
- Karunya Institute of Technology and Sciences, India
| |
Collapse
|
28
|
Yin H, Wu Z, Huang A, Luo J, Liang J, Lin J, Ye Q, Xie M, Ye C, Li X, Wu Y. Automated nailfold capillary density measurement method based on improved YOLOv5. Microvasc Res 2023; 150:104593. [PMID: 37582460 DOI: 10.1016/j.mvr.2023.104593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/17/2023]
Abstract
Nailfold capillary density is an essential physiological parameter for analyzing nailfold health; however, clinical images of the nailfold are taken in many situations, and most clinicians subjectively analyze nailfold images. Therefore, based on the improved "you only look once v5" (YOLOv5) algorithm, this study proposes an automated method for measuring nailfold capillary density. The improved technique can effectively and rapidly detect distal capillaries by incorporating methods or structures such as 9mosaic, spatial pyramid pooling cross-stage partial construction, bilinear interpolation, and efficient intersection over union. First, the modified YOLOv5 algorithm was used to detect nailfold capillaries. Subsequently, the number of distal capillaries was filtered using the 90° method. Finally, the capillary density was calculated. The results showed that the Average Precision (AP)@0.5 value of the proposed approach reached 85.2 %, which was an improvement of 4.93 %, 5.24 %, and 107 % compared with the original YOLOv5, YOLOv6, and simple-faster rapid-region convolutional network (R-CNN), respectively. For different nailfold images, using the density calculated by nailfold experts as a benchmark, the calculated results of the proposed method were consistent with the manually calculated results and superior to those of the original YOLOv5.
Collapse
Affiliation(s)
- Hao Yin
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Zhiwei Wu
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - An Huang
- School of Mechatronic Engineering and Automation, Foshan University, Foshan 528000, China
| | - Jiaxiong Luo
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Junzhao Liang
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Jianan Lin
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Qianyao Ye
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Mugui Xie
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Cong Ye
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Xiaosong Li
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Yanxiong Wu
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China; Ji Hua Laboratory, Foshan, Guangdong 528200, China.
| |
Collapse
|
29
|
Li X, Wang L, Miao H, Zhang S. Aphid Recognition and Counting Based on an Improved YOLOv5 Algorithm in a Climate Chamber Environment. Insects 2023; 14:839. [PMID: 37999038 PMCID: PMC10671967 DOI: 10.3390/insects14110839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023]
Abstract
Due to changes in light intensity, varying degrees of aphid aggregation, and small scales in the climate chamber environment, accurately identifying and counting aphids remains a challenge. In this paper, an improved YOLOv5 aphid detection model based on CNN is proposed to address aphid recognition and counting. First, to reduce the overfitting problem of insufficient data, the proposed YOLOv5 model uses an image enhancement method combining Mosaic and GridMask to expand the aphid dataset. Second, a convolutional block attention mechanism (CBAM) is proposed in the backbone layer to improve the recognition accuracy of aphid small targets. Subsequently, the feature fusion method of bi-directional feature pyramid network (BiFPN) is employed to enhance the YOLOv5 neck, further improving the recognition accuracy and speed of aphids; in addition, a Transformer structure is introduced in front of the detection head to investigate the impact of aphid aggregation and light intensity on recognition accuracy. Experiments have shown that, through the fusion of the proposed methods, the model recognition accuracy and recall rate can reach 99.1%, the value mAP@0.5 can reach 99.3%, and the inference time can reach 9.4 ms, which is significantly better than other YOLO series networks. Moreover, it has strong robustness in actual recognition tasks and can provide a reference for pest prevention and control in climate chambers.
Collapse
Affiliation(s)
| | | | - Hong Miao
- College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
| | | |
Collapse
|
30
|
Meng W, Yuan Y. SGN-YOLO: Detecting Wood Defects with Improved YOLOv5 Based on Semi-Global Network. Sensors (Basel) 2023; 23:8705. [PMID: 37960405 PMCID: PMC10649724 DOI: 10.3390/s23218705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 11/15/2023]
Abstract
Object detection based on wood defects involves using bounding boxes to label defects in the surface image of the wood. This step is crucial before the transformation of wood products. Due to the small size and diverse shape of wood defects, most previous object detection models are unable to filter out critical features effectively. Consequently, they have faced challenges in generating adequate contextual information to detect defects accurately. In this paper, we proposed a YOLOv5 model based on a Semi-Global Network (SGN) to detect wood defects. Unlike previous models, firstly, a lightweight SGN is introduced in the backbone to model the global context, which can improve the accuracy and reduce the complexity of the network at the same time; the backbone is embedded with the Extended Efficient Layer Aggregation Network (E-ELAN), which continuously enhances the learning ability of the network; and finally, the Efficient Intersection and Merger (EIOU) loss is used to solve the problems of slow convergence speed and inaccurate regression results. Experimental results on public wood defect datasets demonstrated that our approach outperformed existing target detection models. The mAP value was 86.4%, a 3.1% improvement over the baseline network model, a 7.1% improvement over SSD, and a 13.6% improvement over Faster R-CNN. These results show the effectiveness of our proposed methodology.
Collapse
Affiliation(s)
- Wei Meng
- College of Information, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China;
| | - Yilin Yuan
- College of Information, Beijing Forestry University, Beijing 100083, China
- Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China;
| |
Collapse
|
31
|
Zhang J, Shi B, Chen B, Chen H, Xu W. A Real-Time Flame Detection Method Using Deformable Object Detection and Time Sequence Analysis. Sensors (Basel) 2023; 23:8616. [PMID: 37896709 PMCID: PMC10611399 DOI: 10.3390/s23208616] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/08/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023]
Abstract
Timely and accurate flame detection is a very important and practical technology for preventing the occurrence of fire accidents effectively. However, the current methods of flame detection are still faced with many challenges in video surveillance scenarios due to issues such as varying flame shapes, imbalanced samples, and interference from flame-like objects. In this work, a real-time flame detection method based on deformable object detection and time sequence analysis is proposed to address these issues. Firstly, based on the existing single-stage object detection network YOLOv5s, the network structure is improved by introducing deformable convolution to enhance the feature extraction ability for irregularly shaped flames. Secondly, the loss function is improved by using Focal Loss as the classification loss function to solve the problems of the imbalance of positive (flames) and negative (background) samples, as well as the imbalance of easy and hard samples, and by using EIOU Loss as the regression loss function to solve the problems of a slow convergence speed and inaccurate regression position in network training. Finally, a time sequence analysis strategy is adopted to comprehensively analyze the flame detection results of the current frame and historical frames in the surveillance video, alleviating false alarms caused by flame shape changes, flame occlusion, and flame-like interference. The experimental results indicate that the average precision (AP) and the F-Measure index of flame detection using the proposed method reach 93.0% and 89.6%, respectively, both of which are superior to the compared methods, and the detection speed is 24-26 FPS, meeting the real-time requirements of video flame detection.
Collapse
Affiliation(s)
- Jingyuan Zhang
- School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; (J.Z.); (B.C.)
- Sureland Industrial Fire Safety Limited, Beijing 101300, China;
| | - Bo Shi
- Sureland Industrial Fire Safety Limited, Beijing 101300, China;
| | - Bin Chen
- School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; (J.Z.); (B.C.)
| | - Heping Chen
- School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; (J.Z.); (B.C.)
| | - Wangming Xu
- School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; (J.Z.); (B.C.)
- Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
| |
Collapse
|
32
|
Li X, Wang X, Ong P, Yi Z, Ding L, Han C. Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream. Sensors (Basel) 2023; 23:8444. [PMID: 37896537 PMCID: PMC10611008 DOI: 10.3390/s23208444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/29/2023]
Abstract
Dragon fruit (Hylocereus undatus) is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient. Deep learning techniques have proven effective for object recognition tasks but limited research has been conducted on dragon fruit due to its unique stem morphology and the coexistence of flowers and fruits. Additionally, the challenge lies in developing a lightweight recognition and tracking model that can be seamlessly integrated into mobile platforms, enabling on-site quantity counting. In this study, a video stream inspection method was proposed to classify and count dragon fruit flowers, immature fruits (green fruits), and mature fruits (red fruits) in a dragon fruit plantation. The approach involves three key steps: (1) utilizing the YOLOv5 network for the identification of different dragon fruit categories, (2) employing the improved ByteTrack object tracking algorithm to assign unique IDs to each target and track their movement, and (3) defining a region of interest area for precise classification and counting of dragon fruit across categories. Experimental results demonstrate recognition accuracies of 94.1%, 94.8%, and 96.1% for dragon fruit flowers, green fruits, and red fruits, respectively, with an overall average recognition accuracy of 95.0%. Furthermore, the counting accuracy for each category is measured at 97.68%, 93.97%, and 91.89%, respectively. The proposed method achieves a counting speed of 56 frames per second on a 1080ti GPU. The findings establish the efficacy and practicality of this method for accurate counting of dragon fruit or other fruit varieties.
Collapse
Affiliation(s)
- Xiuhua Li
- School of Electrical Engineering, Guangxi University, Nanning 530004, China; (X.L.); (X.W.); (L.D.); (C.H.)
- Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China
| | - Xiang Wang
- School of Electrical Engineering, Guangxi University, Nanning 530004, China; (X.L.); (X.W.); (L.D.); (C.H.)
| | - Pauline Ong
- Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Johor, Malaysia;
| | - Zeren Yi
- School of Electrical Engineering, Guangxi University, Nanning 530004, China; (X.L.); (X.W.); (L.D.); (C.H.)
| | - Lu Ding
- School of Electrical Engineering, Guangxi University, Nanning 530004, China; (X.L.); (X.W.); (L.D.); (C.H.)
| | - Chao Han
- School of Electrical Engineering, Guangxi University, Nanning 530004, China; (X.L.); (X.W.); (L.D.); (C.H.)
| |
Collapse
|
33
|
Jiang Y, Yan H, Zhang Y, Wu K, Liu R, Lin C. RDD- YOLOv5: Road Defect Detection Algorithm with Self-Attention Based on Unmanned Aerial Vehicle Inspection. Sensors (Basel) 2023; 23:8241. [PMID: 37837071 PMCID: PMC10575368 DOI: 10.3390/s23198241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/27/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023]
Abstract
Road defect detection is a crucial aspect of road maintenance projects, but traditional manual methods are time-consuming, labor-intensive, and lack accuracy. Leveraging deep learning frameworks for object detection offers a promising solution to these challenges. However, the complexity of backgrounds, low resolution, and similarity of cracks make detecting road cracks with high accuracy challenging. To address these issues, a novel road crack detection algorithm, termed Road Defect Detection YOLOv5 (RDD-YOLOv5), was proposed. Firstly, a model was proposed to integrate the transformer structure and explicit vision center to capture the long-distance dependency and aggregate key characteristics. Additionally, the Sigmoid-weighted linear activations in YOLOv5 were replaced with the Gaussian Error Linear Units to enhance the model's nonlinear fitting capability. To evaluate the algorithm's performance, a UAV flight platform was constructed, and experimental freebies were provided to boost inspection efficiency. The experimental results demonstrate the effectiveness of RDD-YOLOv5, achieving a mean average precision of 91.48%, surpassing the original YOLOv5 by 2.5%. The proposed model proves its ability to accurately identify road cracks, even under challenging and complex traffic backgrounds. This advancement in road crack detection technology has significant implications for improving road maintenance and safety.
Collapse
Affiliation(s)
- Yutian Jiang
- College of Transportation, Jilin University, Changchun 130022, China; (Y.J.); (H.Y.); (R.L.)
| | - Haotian Yan
- College of Transportation, Jilin University, Changchun 130022, China; (Y.J.); (H.Y.); (R.L.)
| | - Yiru Zhang
- College of Communication Engineering, Jilin University, Changchun 130022, China; (Y.Z.); (K.W.)
| | - Keqiang Wu
- College of Communication Engineering, Jilin University, Changchun 130022, China; (Y.Z.); (K.W.)
| | - Ruiyuan Liu
- College of Transportation, Jilin University, Changchun 130022, China; (Y.J.); (H.Y.); (R.L.)
| | - Ciyun Lin
- College of Transportation, Jilin University, Changchun 130022, China; (Y.J.); (H.Y.); (R.L.)
- Jilin Engineering Research Center for Intelligent Transportation System, Changchun 130022, China
| |
Collapse
|
34
|
Chen S, Duan J, Zhang N, Qi M, Li J, Wang H, Wang R, Ju R, Duan Y, Qi S. MSA- YOLOv5: Multi-scale attention-based YOLOv5 for automatic detection of acute ischemic stroke from multi-modality MRI images. Comput Biol Med 2023; 165:107471. [PMID: 37716245 DOI: 10.1016/j.compbiomed.2023.107471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/02/2023] [Accepted: 09/04/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Acute ischemic stroke (AIS) is a common neurological disorder characterized by the sudden onset of cerebral ischemia, leading to functional impairments. Swift and precise detection of AIS lesions is crucial for stroke diagnosis and treatment but poses a significant challenge. This study aims to leverage multimodal fusion technology to combine complementary information from various modalities, thereby enhancing the detection performance of AIS target detection models. METHODS In this retrospective study of AIS, we collected data from 316 AIS patients and created a multi-modality magnetic resonance imaging (MRI) dataset. We propose a Multi-Scale Attention-based YOLOv5 (MSA-YOLOv5), targeting challenges such as small lesion size and blurred borders at low resolutions. Specifically, we augment YOLOv5 with a prediction head to detect objects at various scales. Next, we replace the original prediction head with a Multi-Scale Swin Transformer Prediction Head (MS-STPH), which reduces computational complexity to linear levels and enhances the ability to detect small lesions. We incorporate a Second-Order channel attention (SOCA) module to adaptively rescale channel features by employing second-order feature statistics for more discriminative representations. Finally, we further validate the effectiveness of our method using the ISLES 2022 dataset. RESULTS On our in-house AIS dataset, MSA-YOLOv5 achieves a 79.0% mAP0.5, substantially surpassing other single-stage models. Compared to two-stage models, it maintains a comparable performance level while significantly reducing the number of parameters and resolution. On the ISLES 2022 dataset, MSA-YOLOv5 attains an 80.0% mAP0.5, outperforming other network models by a considerable margin. MS-STPH and SOCA modules can significantly increase mAP0.5 by 2.7% and 1.9%, respectively. Visualization interpretability results show that the proposed MSA-YOLOv5 restricts high attention in the small regions of AIS lesions. CONCLUSIONS The proposed MSA-YOLOv5 is capable of automatically and effectively detecting acute ischemic stroke lesions in multimodal images, particularly for small lesions and artifacts. Our enhanced model reduces the number of parameters while improving detection accuracy. This model can potentially assist radiologists in providing more accurate diagnosis, and enable clinicians to develop better treatment plans.
Collapse
Affiliation(s)
- Shannan Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Jinfeng Duan
- Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, Shenyang, China; Postgraduate College, China Medical University, Shenyang, China.
| | - Nan Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Miao Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Jinze Li
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Hong Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Rongqiang Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Ronghui Ju
- Department of Radiology, The People's Hospital of Liaoning Province, Shenyang, China.
| | - Yang Duan
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| |
Collapse
|
35
|
Liao H, Zhu W. YOLO-DRS: A Bioinspired Object Detection Algorithm for Remote Sensing Images Incorporating a Multi-Scale Efficient Lightweight Attention Mechanism. Biomimetics (Basel) 2023; 8:458. [PMID: 37887591 PMCID: PMC10604743 DOI: 10.3390/biomimetics8060458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
Bioinspired object detection in remotely sensed images plays an important role in a variety of fields. Due to the small size of the target, complex background information, and multi-scale remote sensing images, the generalized YOLOv5 detection framework is unable to obtain good detection results. In order to deal with this issue, we proposed YOLO-DRS, a bioinspired object detection algorithm for remote sensing images incorporating a multi-scale efficient lightweight attention mechanism. First, we proposed LEC, a lightweight multi-scale module for efficient attention mechanisms. The fusion of multi-scale feature information allows the LEC module to completely improve the model's ability to extract multi-scale targets and recognize more targets. Then, we propose a transposed convolutional upsampling alternative to the original nearest-neighbor interpolation algorithm. Transposed convolutional upsampling has the potential to greatly reduce the loss of feature information by learning the feature information dynamically, thereby reducing problems such as missed detections and false detections of small targets by the model. Our proposed YOLO-DRS algorithm exhibits significant improvements over the original YOLOv5s. Specifically, it achieves a 2.3% increase in precision (P), a 3.2% increase in recall (R), and a 2.5% increase in mAP@0.5. Notably, the introduction of the LEC module and transposed convolutional results in a respective improvement of 2.2% and 2.1% in mAP@0.5. In addition, YOLO-DRS only increased the GFLOPs by 0.2. In comparison to the state-of-the-art algorithms, namely YOLOv8s and YOLOv7-tiny, YOLO-DRS demonstrates significant improvements in the mAP@0.5 metrics, with enhancements ranging from 1.8% to 7.3%. It is fully proved that our YOLO-DRS can reduce the missed and false detection problems of remote sensing target detection.
Collapse
Affiliation(s)
| | - Wenqiu Zhu
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China;
| |
Collapse
|
36
|
Yi X, Qian C, Wu P, Maponde BT, Jiang T, Ge W. Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5. Sensors (Basel) 2023; 23:8204. [PMID: 37837034 PMCID: PMC10575358 DOI: 10.3390/s23198204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/14/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took a part-based approach, dividing the identification task into two parts: part detection and identification classification. We proposed an improved bird part detection algorithm based on YOLOv5, which can handle partial overlap and complex environmental conditions between part objects. The backbone network incorporates the Res2Net-CBAM module to enhance the receptive fields of each network layer, strengthen the channel characteristics, and improve the sensitivity of the model to important information. Additionally, in order to boost data on features extraction and channel self-regulation, we have integrated CBAM attention mechanisms into the neck. The success rate of our suggested model, according to experimental findings, is 86.6%, 1.2% greater than the accuracy of the original model. Furthermore, when compared with other algorithms, our model's accuracy shows noticeable improvement. These results show how useful the method we suggested is for quickly and precisely recognizing different bird species.
Collapse
Affiliation(s)
| | | | - Peng Wu
- College of Mathematics & Computer Science, Zhejiang A & F University, Hangzhou 311300, China; (X.Y.); (C.Q.); (B.T.M.); (T.J.); (W.G.)
| | | | | | | |
Collapse
|
37
|
Han T, Dong Q, Sun L. SenseLite: A YOLO-Based Lightweight Model for Small Object Detection in Aerial Imagery. Sensors (Basel) 2023; 23:8118. [PMID: 37836948 PMCID: PMC10574857 DOI: 10.3390/s23198118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
In the field of aerial remote sensing, detecting small objects in aerial images is challenging. Their subtle presence against broad backgrounds, combined with environmental complexities and low image resolution, complicates identification. While their detection is crucial for urban planning, traffic monitoring, and military reconnaissance, many deep learning approaches demand significant computational resources, hindering real-time applications. To elevate the accuracy of small object detection in aerial imagery and cater to real-time requirements, we introduce SenseLite, a lightweight and efficient model tailored for aerial image object detection. First, we innovatively structured the YOLOv5 model for a more streamlined structure. In the backbone, we replaced the original structure with cutting-edge lightweight neural operator Involution, enhancing contextual semantics and weight distribution. For the neck, we incorporated GSConv and slim-Neck, striking a balance between reduced computational complexity and performance, which is ideal for rapid predictions. Additionally, to enhance detection accuracy, we integrated a squeeze-and-excitation (SE) mechanism to amplify channel communication and improve detection accuracy. Finally, the Soft-NMS strategy was employed to manage overlapping targets, ensuring precise concurrent detections. Performance-wise, SenseLite reduces parameters by 30.5%, from 7.05 M to 4.9 M, as well as computational demands, with GFLOPs decreasing from 15.9 to 11.2. It surpasses the original YOLOv5, showing a 5.5% mAP0.5 improvement, 0.9% higher precision, and 1.4% better recall on the DOTA dataset. Compared to other leading methods, SenseLite stands out in terms of performance.
Collapse
Affiliation(s)
| | | | - Lina Sun
- Department of Process Equipment and Control Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (T.H.); (Q.D.)
| |
Collapse
|
38
|
Wang Q, Yang L, Zhou B, Luan Z, Zhang J. YOLO-SS-Large: A Lightweight and High-Performance Model for Defect Detection in Substations. Sensors (Basel) 2023; 23:8080. [PMID: 37836911 PMCID: PMC10575286 DOI: 10.3390/s23198080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/13/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
With the development of deep fusion intelligent control technology and the application of low-carbon energy, the number of renewable energy sources connected to the distribution grid has been increasing year by year, gradually replacing traditional distribution grids with active distribution grids. In addition, as an important component of the distribution grid, substations have a complex internal environment and numerous devices. The problems of untimely defect detection and slow response during intelligent inspections are particularly prominent, posing risks and challenges to the safe and stable operation of active distribution grids. To address these issues, this paper proposes a high-performance and lightweight substation defect detection model called YOLO-Substation-large (YOLO-SS-large) based on YOLOv5m. The model improves lightweight performance based upon the FasterNet network structure and obtains the F-YOLOv5m model. Furthermore, in order to enhance the detection performance of the model for small object defects in substations, the normalized Wasserstein distance (NWD) and complete intersection over union (CIoU) loss functions are weighted and fused to design a novel loss function called NWD-CIoU. Lastly, based on the improved model mentioned above, the dynamic head module is introduced to unify the scale-aware, spatial-aware, and task-aware attention of the object detection heads of the model. Compared to the YOLOv5m model, the YOLO-SS-Large model achieves an average precision improvement of 0.3%, FPS enhancement of 43.5%, and parameter reduction of 41.0%. This improved model demonstrates significantly enhanced comprehensive performance, better meeting the requirements of the speed and precision for substation defect detection, and plays an important role in promoting the informatization and intelligent construction of active distribution grids.
Collapse
Affiliation(s)
- Qian Wang
- Qujiang Campus, School of Electrical Engineering, Xi’an University of Technolgy, Xi’an 710048, China; (L.Y.); (Z.L.); (J.Z.)
| | - Lixin Yang
- Qujiang Campus, School of Electrical Engineering, Xi’an University of Technolgy, Xi’an 710048, China; (L.Y.); (Z.L.); (J.Z.)
| | - Bin Zhou
- North China Electric Power Research Institute Co., Ltd. Xi’an Branch, Xi’an 710000, China;
| | - Zhirong Luan
- Qujiang Campus, School of Electrical Engineering, Xi’an University of Technolgy, Xi’an 710048, China; (L.Y.); (Z.L.); (J.Z.)
| | - Jiawei Zhang
- Qujiang Campus, School of Electrical Engineering, Xi’an University of Technolgy, Xi’an 710048, China; (L.Y.); (Z.L.); (J.Z.)
| |
Collapse
|
39
|
Kumar S, Arif T, Ahamad G, Chaudhary AA, Khan S, Ali MAM. An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5. Diagnostics (Basel) 2023; 13:2978. [PMID: 37761346 PMCID: PMC10527934 DOI: 10.3390/diagnostics13182978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/11/2023] [Accepted: 06/15/2023] [Indexed: 09/29/2023] Open
Abstract
Intestinal parasitic infections pose a grave threat to human health, particularly in tropical and subtropical regions. The traditional manual microscopy system of intestinal parasite detection remains the gold standard procedure for diagnosing parasite cysts or eggs. This approach is costly, time-consuming (30 min per sample), highly tedious, and requires a specialist. However, computer vision, based on deep learning, has made great strides in recent years. Despite the significant advances in deep convolutional neural network-based architectures, little research has been conducted to explore these techniques' potential in parasitology, specifically for intestinal parasites. This research presents a novel proposal for state-of-the-art transfer learning architecture for the detection and classification of intestinal parasite eggs from images. The ultimate goal is to ensure prompt treatment for patients while also alleviating the burden on experts. Our approach comprised two main stages: image pre-processing and augmentation in the first stage, and YOLOv5 algorithms for detection and classification in the second stage, followed by performance comparison based on different parameters. Remarkably, our algorithms achieved a mean average precision of approximately 97% and a detection time of only 8.5 ms per sample for a dataset of 5393 intestinal parasite images. This innovative approach holds tremendous potential to form a solid theoretical basis for real-time detection and classification in routine clinical examinations, addressing the increasing demand and accelerating the diagnostic process. Our research contributes to the development of cutting-edge technologies for the efficient and accurate detection of intestinal parasite eggs, advancing the field of medical imaging and diagnosis.
Collapse
Affiliation(s)
- Satish Kumar
- Department of Information Technology, BGSB University, Rajouri 185131, India
| | - Tasleem Arif
- Department of Information Technology, BGSB University, Rajouri 185131, India
| | - Gulfam Ahamad
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri 185131, India
| | - Anis Ahmad Chaudhary
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia
| | - Salahuddin Khan
- Department of Biochemistry, College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia
| | - Mohamed A. M. Ali
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia
- Department of Biochemistry, Faculty of Science, Ain Shams University, Cairo 11566, Egypt
| |
Collapse
|
40
|
Liu R, Liu T, Dan T, Yang S, Li Y, Luo B, Zhuang Y, Fan X, Zhang X, Cai H, Teng Y. AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images. Patterns (N Y) 2023; 4:100806. [PMID: 37720337 PMCID: PMC10499858 DOI: 10.1016/j.patter.2023.100806] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/02/2023] [Accepted: 07/07/2023] [Indexed: 09/19/2023]
Abstract
Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.
Collapse
Affiliation(s)
- Ruicun Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Tuoyu Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Tingting Dan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510600, China
| | - Shan Yang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Yanbing Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Boyu Luo
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Yingtan Zhuang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Xinyue Fan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Xianchao Zhang
- Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Jiaxing University, Jiaxing 314001, China
- Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, Jiaxing 314001, China
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510600, China
| | - Yue Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| |
Collapse
|
41
|
Zhou Z, Qiu Q, Liu H, Ge X, Li T, Xing L, Yang R, Yin Y. Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model. Cancers (Basel) 2023; 15:4443. [PMID: 37760413 PMCID: PMC10526374 DOI: 10.3390/cancers15184443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients' survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis.
Collapse
Affiliation(s)
- Zichun Zhou
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
| | - Qingtao Qiu
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
| | - Huiling Liu
- Department of Oncology, Binzhou People’s Hospital, Binzhou 256610, China
- Third Clinical Medical College, Xinjiang Medical University, Urumqi 830011, China
| | - Xuanchu Ge
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Tengxiang Li
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Ligang Xing
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Runtao Yang
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
| | - Yong Yin
- Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| |
Collapse
|
42
|
Lv W, Chen T, Zeng Y, Liu W, Huang C. A challenge of deep-learning-based object detection for hair follicle dataset. J Cosmet Dermatol 2023; 22:2565-2578. [PMID: 37021716 DOI: 10.1111/jocd.15742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/24/2023] [Accepted: 03/14/2023] [Indexed: 04/07/2023]
Abstract
BACKGROUND Deep-learning object detection has been applied in various industries, including healthcare, to address hair loss. METHODS In this paper, YOLOv5 object detection algorithm was used to detect hair follicles in a small and specific image dataset collected using a specialized camera on the scalp of individuals with different ages, regions, and genders. The performance of YOLOv5 was compared with other popular object detection models. RESULTS YOLOv5 performed well in the detection of hair follicles, and the follicles were classified into five classes based on the number of hairs and the type of hair contained. In single-class object detection experiments, a smaller batch size and the smallest YOLOv5s model achieved the best results, with an map of 0.8151. In multiclass object detection experiments, the larger YOLOv5l model was able to achieve the best results, and batch size affected the result of model training. CONCLUSION YOLOv5 is a promising algorithm for detecting hair follicles in a small and specific image dataset, and its performance is comparable to other popular object detection models. However, the challenges of small-scale data and sample imbalance need to be addressed to improve the performance of target detection algorithms.
Collapse
Affiliation(s)
- Wei Lv
- Department of Alibaba Cloud Big Data Application, Zhuhai College of Science and Technology, Zhuhai, China
| | - Tao Chen
- Department of Alibaba Cloud Big Data Application, Zhuhai College of Science and Technology, Zhuhai, China
| | - Yifan Zeng
- Department of Alibaba Cloud Big Data Application, Zhuhai College of Science and Technology, Zhuhai, China
| | - Weihong Liu
- Department of Alibaba Cloud Big Data Application, Zhuhai College of Science and Technology, Zhuhai, China
| | - Chuying Huang
- Department of Alibaba Cloud Big Data Application, Zhuhai College of Science and Technology, Zhuhai, China
| |
Collapse
|
43
|
Zhao Y, Chen B, Liu B, Yu C, Wang L, Wang S. GRP- YOLOv5: An Improved Bearing Defect Detection Algorithm Based on YOLOv5. Sensors (Basel) 2023; 23:7437. [PMID: 37687893 PMCID: PMC10490579 DOI: 10.3390/s23177437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/09/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023]
Abstract
Currently, most chemical transmission equipment relies on bearings to support rotating shafts and to transmit power. However, bearing defects can lead to a series of failures in the equipment, resulting in reduced production efficiency. To prevent such occurrences, this paper proposes an improved bearing defect detection algorithm based on YOLOv5. Firstly, to mitigate the influence of the similarity between bearing defects and non-defective regions on the detection performance, gamma transformation is introduced in the preprocessing stage of the model to adjust the image's grayscale and contrast. Secondly, to better capture the details and semantic information of the defects, this approach incorporates the ResC2Net model with a residual-like structure during the feature-extraction stage, enabling more nonlinear transformations and channel interaction operations so as to enhance the model's perception and representation capabilities of the defect targets. Additionally, PConv convolution is added in the feature fusion part to increase the network depth and better capture the detailed information of defects while maintaining time complexity. The experimental results demonstrate that the GRP-YOLOv5 model achieves a mAP@0.5 of 93.5%, a mAP@0.5:0.95 of 52.7%, and has a model size of 25 MB. Compared to other experimental models, GRP-YOLOv5 exhibits excellent performance in bearing defect detection accuracy. However, the model's FPS (frames per second) performance is not satisfactory. Despite its small size of 25 MB, the processing speed is relatively slow, which may have some impact on real-time or high-throughput applications. This limitation should be considered in future research and in the optimization efforts to improve the overall performance of the model.
Collapse
Affiliation(s)
- Yue Zhao
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Bolun Chen
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China
- Department of Physics, University of Fribourg, CH-1700 Fribourg, Switzerland
| | - Bushi Liu
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Cuiying Yu
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Ling Wang
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| | - Shanshan Wang
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China
| |
Collapse
|
44
|
Jaihuni M, Gan H, Tabler T, Prado M, Qi H, Zhao Y. Broiler Mobility Assessment via a Semi-Supervised Deep Learning Model and Neo-Deep Sort Algorithm. Animals (Basel) 2023; 13:2719. [PMID: 37684983 PMCID: PMC10487264 DOI: 10.3390/ani13172719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/11/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Mobility is a vital welfare indicator that may influence broilers' daily activities. Classical broiler mobility assessment methods are laborious and cannot provide timely insights into their conditions. Here, we proposed a semi-supervised Deep Learning (DL) model, YOLOv5 (You Only Look Once version 5), combined with a deep sort algorithm conjoined with our newly proposed algorithm, neo-deep sort, for individual broiler mobility tracking. Initially, 1650 labeled images from five days were employed to train the YOLOv5 model. Through semi-supervised learning (SSL), this narrowly trained model was then used for pseudo-labeling 2160 images, of which 2153 were successfully labeled. Thereafter, the YOLOv5 model was fine-tuned on the newly labeled images. Lastly, the trained YOLOv5 and the neo-deep sort algorithm were applied to detect and track 28 broilers in two pens and categorize them in terms of hourly and daily travel distances and speeds. SSL helped in increasing the YOLOv5 model's mean average precision (mAP) in detecting birds from 81% to 98%. Compared with the manually measured covered distances of broilers, the combined model provided individual broilers' hourly moved distances with a validation accuracy of about 80%. Eventually, individual and flock-level mobilities were quantified while overcoming the occlusion, false, and miss-detection issues.
Collapse
Affiliation(s)
- Mustafa Jaihuni
- Department of Animal Science, University of Tennessee, Knoxville, TN 37996, USA; (M.J.); (T.T.); (M.P.)
| | - Hao Gan
- Department of Biosystems Engineering, University of Tennessee, Knoxville, TN 37996, USA;
| | - Tom Tabler
- Department of Animal Science, University of Tennessee, Knoxville, TN 37996, USA; (M.J.); (T.T.); (M.P.)
| | - Maria Prado
- Department of Animal Science, University of Tennessee, Knoxville, TN 37996, USA; (M.J.); (T.T.); (M.P.)
| | - Hairong Qi
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA;
| | - Yang Zhao
- Department of Animal Science, University of Tennessee, Knoxville, TN 37996, USA; (M.J.); (T.T.); (M.P.)
| |
Collapse
|
45
|
Xu H, Pan H, Li J. Surface Defect Detection of Bearing Rings Based on an Improved YOLOv5 Network. Sensors (Basel) 2023; 23:7443. [PMID: 37687898 PMCID: PMC10490562 DOI: 10.3390/s23177443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023]
Abstract
Considering the characteristics of complex texture backgrounds, uneven brightness, varying defect sizes, and multiple defect types of the bearing surface images, a surface defect detection method for bearing rings is proposed based on improved YOLOv5. First, replacing the C3 module in the backbone network with a C2f module can effectively reduce the number of network parameters and computational complexity, thereby improving the speed and accuracy of the backbone network. Second, adding the SPD module into the backbone and neck networks enhances their ability to process low-resolution and small-object images. Next, replacing the nearest-neighbor upsampling with the lightweight and universal CARAFE operator fully utilizes feature semantic information, enriches contextual information, and reduces information loss during transmission, thereby effectively improving the model's diversity and robustness. Finally, we constructed a dataset of bearing ring surface images collected from industrial sites and conducted numerous experiments based on this dataset. Experimental results show that the mean average precision (mAP) of the network is 97.3%, especially for dents and black spot defects, improved by 2.2% and 3.9%, respectively, and that the detection speed can reach 100 frames per second (FPS). Compared with mainstream surface defect detection algorithms, the proposed method shows significant improvements in both accuracy and detection time and can meet the requirements of industrial defect detection.
Collapse
Affiliation(s)
- Haitao Xu
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.X.); (J.L.)
- Changshan Research Institute, Zhejiang Sci-Tech University, Quzhou 324299, China
| | - Haipeng Pan
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.X.); (J.L.)
- Changshan Research Institute, Zhejiang Sci-Tech University, Quzhou 324299, China
| | - Junfeng Li
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.X.); (J.L.)
- Changshan Research Institute, Zhejiang Sci-Tech University, Quzhou 324299, China
| |
Collapse
|
46
|
Sun Y, Zhang D, Guo X, Yang H. Lightweight Algorithm for Apple Detection Based on an Improved YOLOv5 Model. Plants (Basel) 2023; 12:3032. [PMID: 37687279 PMCID: PMC10490290 DOI: 10.3390/plants12173032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023]
Abstract
The detection algorithm of the apple-picking robot contains a complex network structure and huge parameter volume, which seriously limits the inference speed. To enable automatic apple picking in complex unstructured environments based on embedded platforms, we propose a lightweight YOLOv5-CS model for apple detection based on YOLOv5n. Firstly, we introduced the lightweight C3-light module to replace C3 to enhance the extraction of spatial features and boots the running speed. Then, we incorporated SimAM, a parameter-free attention module, into the neck layer to improve the model's accuracy. The results showed that the size and inference speed of YOLOv5-CS were 6.25 MB and 0.014 s, which were 45 and 1.2 times that of the YOLOv5n model, respectively. The number of floating-point operations (FLOPs) were reduced by 15.56%, and the average precision (AP) reached 99.1%. Finally, we conducted extensive experiments, and the results showed that the YOLOv5-CS outperformed mainstream networks in terms of AP, speed, and model size. Thus, our real-time YOLOv5-CS model detects apples in complex orchard environments efficiently and provides technical support for visual recognition systems for intelligent apple-picking devices.
Collapse
Affiliation(s)
- Yu Sun
- College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China; (Y.S.); (D.Z.); (X.G.)
| | - Dongwei Zhang
- College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China; (Y.S.); (D.Z.); (X.G.)
| | - Xindong Guo
- College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China; (Y.S.); (D.Z.); (X.G.)
- College of Computer Science and Technology, North University of China, Taiyuan 030051, China
| | - Hua Yang
- College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China; (Y.S.); (D.Z.); (X.G.)
| |
Collapse
|
47
|
Xu Y, Nie J, Cen H, Wen B, Liu S, Li J, Ge J, Yu L, Pu Y, Song K, Liu Z, Cai Q. Spatio-Temporal-Based Identification of Aggressive Behavior in Group Sheep. Animals (Basel) 2023; 13:2636. [PMID: 37627427 PMCID: PMC10451720 DOI: 10.3390/ani13162636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
In order to solve the problems of low efficiency and subjectivity of manual observation in the process of group-sheep-aggression detection, we propose a video streaming-based model for detecting aggressive behavior in group sheep. In the experiment, we collected videos of the sheep's daily routine and videos of the aggressive behavior of sheep in the sheep pen. Using the open-source software LabelImg, we labeled the data with bounding boxes. Firstly, the YOLOv5 detects all sheep in each frame of the video and outputs the coordinates information. Secondly, we sort the sheep's coordinates using a sheep tracking heuristic proposed in this paper. Finally, the sorted data are fed into an LSTM framework to predict the occurrence of aggression. To optimize the model's parameters, we analyze the confidence, batch size and skipping frame. The best-performing model from our experiments has 93.38% Precision and 91.86% Recall. Additionally, we compare our video streaming-based model with image-based models for detecting aggression in group sheep. In sheep aggression, the video stream detection model can solve the false detection phenomenon caused by head impact feature occlusion of aggressive sheep in the image detection model.
Collapse
Affiliation(s)
- Yalei Xu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Jing Nie
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| | - Honglei Cen
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| | - Baoqin Wen
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| | - Shuangyin Liu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Jingbin Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| | - Jianbing Ge
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| | - Longhui Yu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
- College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Yuhai Pu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| | - Kangle Song
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| | - Zichen Liu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| | - Qiang Cai
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China (Z.L.); (Q.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China
- Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China
| |
Collapse
|
48
|
Xu X, Shi J, Chen Y, He Q, Liu L, Sun T, Ding R, Lu Y, Xue C, Qiao H. Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level. Front Plant Sci 2023; 14:1200901. [PMID: 37645464 PMCID: PMC10461631 DOI: 10.3389/fpls.2023.1200901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/10/2023] [Indexed: 08/31/2023]
Abstract
Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii.
Collapse
Affiliation(s)
- Xin Xu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Jing Shi
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Yongqin Chen
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Qiang He
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Tong Sun
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Ruifeng Ding
- Institute of Plant Protection, Xinjiang Academy of Agricultural Sciences, Urumqi, China
| | - Yanhui Lu
- Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chaoqun Xue
- Zhengzhou Tobacco Research Institute of China National Tobacco Corporation (CNTC), Zhengzhou, China
| | - Hongbo Qiao
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| |
Collapse
|
49
|
Wu S, Wang J, Liu L, Chen D, Lu H, Xu C, Hao R, Li Z, Wang Q. Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult Rhynchophorus ferrugineus. Insects 2023; 14:698. [PMID: 37623408 PMCID: PMC10455671 DOI: 10.3390/insects14080698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/27/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023]
Abstract
The red palm weevil (RPW, Rhynchophorus ferrugineus) is an invasive and highly destructive pest that poses a serious threat to palm plants. To improve the efficiency of adult RPWs' management, an enhanced YOLOv5 object detection algorithm based on an attention mechanism is proposed in this paper. Firstly, the detection capabilities for small targets are enhanced by adding a convolutional layer to the backbone network of YOLOv5 and forming a quadruple down-sampling layer by splicing and down-sampling the convolutional layers. Secondly, the Squeeze-and-Excitation (SE) attention mechanism and Convolutional Block Attention Module (CBAM) attention mechanism are inserted directly before the SPPF structure to improve the feature extraction capability of the model for targets. Then, 2600 images of RPWs in different scenes and forms are collected and organized for data support. These images are divided into a training set, validation set and test set following a ratio of 7:2:1. Finally, an experiment is conducted, demonstrating that the enhanced YOLOv5 algorithm achieves an average precision of 90.1% (mAP@0.5) and a precision of 93.8% (P), which is a significant improvement compared with related models. In conclusion, the enhanced model brings a higher detection accuracy and real-time performance to the RPW-controlled pest pre-detection system, which helps us to take timely preventive and control measures to avoid serious pest infestation. It also provides scalability for other pest pre-detection systems; with the corresponding dataset and training, the algorithm can be adapted to the detection tasks of other pests, which in turn brings a wider range of applications in the field of monitoring and control of agricultural pests.
Collapse
Affiliation(s)
- Shuai Wu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Jianping Wang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Li Liu
- Hainan Key Laboratory of Tropical Oil Crops Biology, Coconut Research Institute of Chinese Academy of Tropical Agricultural Sciences, Wenchang 571339, China
| | - Danyang Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China
| | - Huimin Lu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China
| | - Chao Xu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Rui Hao
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhao Li
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Qingxuan Wang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| |
Collapse
|
50
|
Lin Y, Zhang J, Jiang Z, Tang Y. YOLOv5-Atn: An Algorithm for Residual Film Detection in Farmland Combined with an Attention Mechanism. Sensors (Basel) 2023; 23:7035. [PMID: 37631572 PMCID: PMC10460032 DOI: 10.3390/s23167035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023]
Abstract
The application of mulching film has significantly contributed to improving agricultural output and benefits, but residual film has caused severe impacts on agricultural production and the environment. In order to realize the accurate recycling of agricultural residual film, the detection of residual film is the first problem to be solved. The difference in color and texture between residual film and bare soil is not obvious, and residual film is of various sizes and morphologies. To solve these problems, the paper proposes a method for detecting residual film in agricultural fields that uses the attention mechanism. First, a two-stage pre-training approach with strengthened memory is proposed to enable the model to better understand the residual film features with limited data. Second, a multi-scale feature fusion module with adaptive weights is proposed to enhance the recognition of small targets of residual film by using attention. Finally, an inter-feature cross-attention mechanism that can realize full interaction between shallow and deep feature information to reduce the useless noise extracted from residual film images is designed. The experimental results on a self-made residual film dataset show that the improved model improves precision, recall, and mAP by 5.39%, 2.02%, and 3.95%, respectively, compared with the original model, and it also outperforms other recent detection models. The method provides strong technical support for accurately identifying farmland residual film and has the potential to be applied to mechanical equipment for the recycling of residual film.
Collapse
Affiliation(s)
- Ying Lin
- College of Software, Xinjiang University, Urumqi 830091, China;
| | - Jianjie Zhang
- College of Software, Xinjiang University, Urumqi 830091, China;
| | - Zhangzhen Jiang
- College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
| | - Yiyu Tang
- College of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
| |
Collapse
|