Detecting tea tree pests in complex backgrounds using a hybrid architecture guided by transformers and multi-scale attention mechanism

BACKGROUND

Tea pests pose a significant threat to tea leaf yield and quality, necessitating fast and accurate detection methods to improve pest control efficiency and reduce economic losses for tea farmers. However, in real tea gardens, some tea pests are small in size and easily camouflaged by complex backgrounds, making it challenging for farmers to promptly and accurately identify them.

RESULTS

To address this issue, we propose a real-time detection method based on TP-YOLOX for monitoring tea pests in complex backgrounds. Our approach incorporates the CSBLayer module, which combines convolution and multi-head self-attention mechanisms, to capture global contextual information from images and expand the network's perception field. Additionally, we integrate an efficient multi-scale attention module to enhance the model's ability to perceive fine details in small targets. To expedite model convergence and improve the precision of target localization, we employ the SIOU loss function as the bounding box regression function. Experimental results demonstrate that TP-YOLOX achieves a significant performance improvement with a relatively small additional computational cost (0.98 floating-point operations), resulting in a 4.50% increase in mean average precision (mAP) compared to the original YOLOX-s. When compared with existing object detection algorithms, TP-YOLOX outperforms them in terms of mAP performance. Moreover, the proposed method achieves a frame rate of 82.66 frames per second, meeting real-time requirements.

CONCLUSION

TP-YOLOX emerges as a proficient solution, capable of accurately and swiftly identifying tea pests amidst the complex backgrounds of tea gardens. This contribution not only offers valuable insights for tea pest monitoring but also serves as a reference for achieving precise pest control. © 2023 Society of Chemical Industry.

[A multiscale carotid plaque detection method based on two-stage analysis]

OBJECTIVE

To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.

METHODS

We proposed a two-stage carotid plaque detection method based on deep convolutional neural network (SM-YOLO).A series of algorithms such as median filtering, histogram equalization, and Gamma transformation were used to preprocess the dataset to improve image quality. In the first stage of the model construction, a candidate plaque set was built based on the YOLOX_l target detection network, using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes. In the second stage, the Histogram of Oriented Gradient (HOG) features and Local Binary Pattern (LBP) features were extracted and fused, and a Support Vector Machine (SVM) classifier was used to screen the candidate plaque set to obtain the final detection results. This model was compared quantitatively and visually with several target detection models (YOLOX_l, SSD, EfficientDet, YOLOV5_l, Faster R-CNN).

RESULTS

SM-YOLO achieved a recall of 89.44%, an accuracy of 90.96%, a F1-Score of 90.19%, and an AP of 92.70% on the test set, outperforming other models in all performance indicators and visual effects. The constructed model had a much shorter detection time than the Faster R-CNN model (only one third of that of the latter), thus meeting the requirements of real-time detection.

CONCLUSION

The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

An intelligent identification and classification system of decoration waste based on deep learning model

Efficient sorting and recycling of decoration waste are crucial for the industry's transformation, upgrading, and high-quality development. However, decoration waste can contain toxic materials and has greatly varying compositions. The traditional method of manual sorting for decoration waste is inefficient and poses health risks to sorting workers. It is therefore imperative to develop an accurate and efficient intelligent classification method to address these issues. To meet the demand for intelligent identification and classification of decoration waste, this paper applied the deep learning method You Only Look Once X (YOLOX) to the task and proposed an identification and classification framework of decoration waste (YOLOX-DW framework). The proposed framework was validated and compared using a multi-label image dataset of decoration waste, and a robot automatic sorting system was constructed for practical sorting experiments. The research results show that the proposed framework achieved a mean average precision (mAP) of 99.16 % for different components of decoration waste, with a detection speed of 39.23 FPS. Its classification efficiency on the robot sorting experimental platform reached 95.06 %, indicating a high potential for application and promotion. This provides a strategy for the intelligent detection, identification, and classification of decoration waste.

Deep-Orga: An improved deep learning-based lightweight model for intestinal organoid detection

PROBLEM

Organoids are 3D cultures that are commonly used for biological and medical research in vitro due to their functional and structural similarity to source organs. The development of organoids can be assessed by morphological tests. However, manual analysis of organoid morphology requires intensive labor from professionals and is prone to observer discrepancies.

AIM

Computer-assisted methods alleviate the pressure of manual labor, especially with the development of deep learning, the performance of morphological detection has been further improved. The aim of this paper is to automate the assessment of organoid morphology using deep learning techniques to reduce the labor pressure of professionals.

METHODS

Based on the lightweight model YOLOX, a lightweight intestinal organoid detection model named Deep-Orga is proposed. First, the performance of the Deep-Orga model is compared with other classical models on the intestinal organoids dataset. Then, ablation experiments are used to validate the improvement of the model detection performance by the improved module. Finally, Deep-Orga is compared with other methods.

RESULTS

Deep-Orga achieves optimal organoid detection with a partial increase in computational effort. Using Deep-Orga to replace the manual analysis process provides a new automated method for organoid morphology evaluation.

CONCLUSION

Deep-Orga proposed in this paper is able to accurately assess organoid development, effectively relieving the labor pressure of professionals and avoiding the subjectivity of assessment. This paper demonstrates the potential application of deep learning in the field of organoid morphology analysis.

Object Detection Based on Lightweight YOLOX for Autonomous Driving

Accurate and rapid response in complex driving scenarios is a challenging problem in autonomous driving. If a target is detected, the vehicle will not be able to react in time, resulting in fatal safety accidents. Therefore, the application of driver assistance systems requires a model that can accurately detect targets in complex scenes and respond quickly. In this paper, a lightweight feature extraction model, ShuffDet, is proposed to replace the CSPDark53 model used by YOLOX by improving the YOLOX algorithm. At the same time, an attention mechanism is introduced into the path aggregation feature pyramid network (PAFPN) to make the network focus more on important information in the network, thereby improving the accuracy of the model. This model, which combines two methods, is called ShuffYOLOX, and it can improve the accuracy of the model while keeping it lightweight. The performance of the ShuffYOLOX model on the KITTI dataset is tested in this paper, and the experimental results show that compared to the original network, the mean average precision (mAP) of the ShuffYOLOX model on the KITTI dataset reaches 92.20%. In addition, the number of parameters of the ShuffYOLOX model is reduced by 34.57%, the Gflops are reduced by 42.19%, and the FPS is increased by 65%. Therefore, the ShuffYOLOX model is very suitable for autonomous driving applications.

Identification of Driver Status Hazard Level and the System

According to the survey statistics, most traffic accidents are caused by the driver's behavior and status irregularities. Because there is no multi-level dangerous state grading system at home and abroad, this paper proposes a complex state grading system for real-time detection and dynamic tracking of the driver's state. The system uses OpenMV as the acquisition camera combined with the cradle head tracking system to collect the driver's current driving image in real-time dynamically, combines the YOLOX algorithm with the OpenPose algorithm to judge the driver's dangerous driving behavior by detecting unsafe objects in the cab and the driver's posture, and combines the improved Retinaface face detection algorithm with the Dlib feature-point algorithm to discriminate the fatigue driving state of the driver. The experimental results show that the accuracy of the three driver danger levels (R1, R2, and R3) obtained by the proposed system reaches 95.8%, 94.5%, and 96.3%, respectively. The experimental results of this system have a specific practical significance in driver-distracted driving warnings.

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family

Introduction

Insect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.

Methods

To address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.

Results

Experimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.

Discussion

Our model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment.

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Endometrial polyps are common gynecological lesions. The standard treatment for this condition is hysteroscopic polypectomy. However, this procedure may be accompanied by misdetection of endometrial polyps. To improve the diagnostic accuracy and reduce the risk of misdetection, a deep learning model based on YOLOX is proposed to detect endometrial polyps in real time. Group normalization is employed to improve its performance with large hysteroscopic images. In addition, we propose a video adjacent-frame association algorithm to address the problem of unstable polyp detection. Our proposed model was trained on a dataset of 11,839 images from 323 cases provided by a hospital and was tested on two datasets of 431 cases from two hospitals. The results show that the lesion-based sensitivity of the model reached 100% and 92.0% for the two test sets, compared with 95.83% and 77.33%, respectively, for the original YOLOX model. This demonstrates that the improved model may be used effectively as a diagnostic tool during clinical hysteroscopic procedures to reduce the risk of missing endometrial polyps.

CF-YOLOX: An Autonomous Driving Detection Model for Multi-Scale Object Detection

In self-driving cars, object detection algorithms are becoming increasingly important, and the accurate and fast recognition of objects is critical to realize autonomous driving. The existing detection algorithms are not ideal for the detection of small objects. This paper proposes a YOLOX-based network model for multi-scale object detection tasks in complex scenes. This method adds a CBAM-G module to the backbone of the original network, which performs grouping operations on CBAM. It changes the height and width of the convolution kernel of the spatial attention module to 7 × 1 to improve the ability of the model to extract prominent features. We proposed an object-contextual feature fusion module, which can provide more semantic information and improve the perception of multi-scale objects. Finally, we considered the problem of fewer samples and less loss of small objects and introduced a scaling factor that could increase the loss of small objects to improve the detection ability of small objects. We validated the effectiveness of the proposed method on the KITTI dataset, and the mAP value was 2.46% higher than the original model. Experimental comparisons showed that our model achieved superior detection performance compared to other models.

Comparison of Pre-Trained YOLO Models on Steel Surface Defects Detector Based on Transfer Learning with GPU-Based Embedded Devices

Steel is one of the most basic ingredients, which plays an important role in the machinery industry. However, the steel surface defects heavily affect its quality. The demand for surface defect detectors draws much attention from researchers all over the world. However, there are still some drawbacks, e.g., the dataset is limited accessible or small-scale public, and related works focus on developing models but do not deeply take into account real-time applications. In this paper, we investigate the feasibility of applying stage-of-the-art deep learning methods based on YOLO models as real-time steel surface defect detectors. Particularly, we compare the performance of YOLOv5, YOLOX, and YOLOv7 while training them with a small-scale open-source NEU-DET dataset on GPU RTX 2080. From the experiment results, YOLOX-s achieves the best accuracy of 89.6% mAP on the NEU-DET dataset. Then, we deploy the weights of trained YOLO models on Nvidia devices to evaluate their real-time performance. Our experiments devices consist of Nvidia Jetson Nano and Jetson Xavier AGX. We also apply some real-time optimization techniques (i.e., exporting to TensorRT, lowering the precision to FP16 or INT8 and reducing the input image size to 320 × 320) to reduce detection speed (fps), thus also reducing the mAP accuracy.

A Domestic Trash Detection Model Based on Improved YOLOX

Domestic trash detection is an essential technology toward achieving a smart city. Due to the complexity and variability of urban trash scenarios, the existing trash detection algorithms suffer from low detection rates and high false positives, as well as the general problem of slow speed in industrial applications. This paper proposes an i-YOLOX model for domestic trash detection based on deep learning algorithms. First, a large number of real-life trash images are collected into a new trash image dataset. Second, the lightweight operator involution is incorporated into the feature extraction structure of the algorithm, which allows the feature extraction layer to establish long-distance feature relationships and adaptively extract channel features. In addition, the ability of the model to distinguish similar trash features is strengthened by adding the convolutional block attention module (CBAM) to the enhanced feature extraction network. Finally, the design of the involution residual head structure in the detection head reduces the gradient disappearance and accelerates the convergence of the model loss values allowing the model to perform better classification and regression of the acquired feature layers. In this study, YOLOX-S is chosen as the baseline for each enhancement experiment. The experimental results show that compared with the baseline algorithm, the mean average precision (mAP) of i-YOLOX is improved by 1.47%, the number of parameters is reduced by 23.3%, and the FPS is improved by 40.4%. In practical applications, this improved model achieves accurate recognition of trash in natural scenes, which further validates the generalization performance of i-YOLOX and provides a reference for future domestic trash detection research.

Improved Algorithm for Insulator and Its Defect Detection Based on YOLOX

Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect detection, this paper concerns the shortcomings of IoU and the sensitivity of small targets to the model regression accuracy. An improved SIoU loss function was proposed based on the regular influence of regression direction on the accuracy. This loss function can accelerate the convergence of the model and make it achieve better results in regressions. For complex backgrounds, ECA (Efficient Channel Attention Module) is embedded between the backbone and the feature fusion layer of the model to reduce the influence of redundant features on the detection accuracy and make progress in the aspect. As a result, these experiments show that the improved model achieved 97.18% mAP which is 2.74% higher than before, and the detection speed could reach 71 fps. To some extent, it can detect insulator and its defects accurately and in real-time.

PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites

With numerous countermeasures, the number of deaths in the construction industry is still higher compared to other industries. Personal Protective Equipment (PPE) is constantly being improved to avoid these accidents, although workers intentionally or unintentionally forget to use such safety measures. It is challenging to manually run a safety check as the number of co-workers on a site can be large; however, it is a prime duty of the authority to provide maximum protection to the workers on the working site. From these motivations, we have created a computer vision (CV) based automatic PPE detection system that detects various types of PPE. This study also created a novel dataset named CHVG (four colored hardhats, vest, safety glass) containing eight different classes, including four colored hardhats, vest, safety glass, person body, and person head. The dataset contains 1,699 images and corresponding annotations of these eight classes. For the detection algorithm, this study has used the You Only Look Once (YOLO) family's anchor-free architecture, YOLOX, which yields better performance than the other object detection models within a satisfactory time interval. Moreover, this study found that the YOLOX-m model yields the highest mean average precision (mAP) than the other three versions of the YOLOX.

A Novel Auto-Synthesis Dataset Approach for Fitting Recognition Using Prior Series Data

To address power transmission line (PTL) traversing complex environments leading to data collection being difficult and costly, we propose a novel auto-synthesis dataset approach for fitting recognition using prior series data. The approach mainly includes three steps: (1) formulates synthesis rules by the prior series data; (2) renders 2D images based on the synthesis rules utilizing advanced virtual 3D techniques; (3) generates the synthetic dataset with images and annotations obtained by processing images using the OpenCV. The trained model using the synthetic dataset was tested by the real dataset (including images and annotations) with a mean average precision (mAP) of 0.98, verifying the feasibility and effectiveness of the proposed approach. The recognition accuracy by the test is comparable with training by real samples and the cost is greatly reduced to generate synthetic datasets. The proposed approach improves the efficiency of establishing a dataset, providing a training data basis for deep learning (DL) of fitting recognition.

Rapid Detection of Wheat Ears in Orthophotos From Unmanned Aerial Vehicles in Fields Based on YOLOX

Wheat ears in unmanned aerial vehicles (UAV) orthophotos are characterized by occlusion, small targets, dense distribution, and complex backgrounds. Rapid identification of wheat ears in UAV orthophotos in a field environment is critical for wheat yield prediction. Three improvements were achieved based on YOLOX-m: mosaic optimized, using BiFPN structure, and attention mechanism, then ablation experiments were performed to verify the effect of each improvement. Three scene datasets were established: images were acquired during three different growing periods, at three planting densities, and under three scenarios of UAV flight heights. In ablation experiments, three improvements had increased recognition accuracies on the experimental dataset. Compared the accuracy of the standard model with our improved model on three scene datasets. Our improved model during three different periods, at three planting densities, and under three scenarios of the UAV flight height, obtaining 88.03%, 87.59%, and 87.93% accuracies, which were, respectively, 2.54%, 1.89%, and 2.15% better than the original model. The results of this study showed that the improved YOLOX-m model can achieve UAV orthophoto wheat recognition under different practical scenarios in large fields, and that the best combination were obtained images from the wheat milk stage, low planting density, and low flight altitude.