A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system

A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval system

Trypanosomiasis, a significant health concern in South America, South Asia, and Southeast Asia, requires active surveys to effectively control the disease. To address this, we have developed a hybrid model that combines deep metric learning (DML) and image retrieval. This model is proficient at identifying Trypanosoma species in microscopic images of thin-blood film examinations. Utilizing the ResNet50 backbone neural network, a trained-model has demonstrated outstanding performance, achieving an accuracy exceeding 99.71 % and up to 96 % in recall. Acknowledging the necessity for automated tools in field scenarios, we demonstrated the potential of our model as an autonomous screening approach. This was achieved by using prevailing convolutional neural network (CNN) applications, and vector database based-images returned by the KNN algorithm. This achievement is primarily attributed to the implementation of the Triplet Margin Loss function as 98 % of precision. The robustness of the model demonstrated in five-fold cross-validation highlights the ResNet50 neural network, based on DML, as a state-of-the-art CNN model as AUC >98 %. The adoption of DML significantly improves the performance of the model, remaining unaffected by variations in the dataset and rendering it a useful tool for fieldwork studies. DML offers several advantages over conventional classification model to manage large-scale datasets with a high volume of classes, enhancing scalability. The model has the capacity to generalize to novel classes that were not encountered during training, proving particularly advantageous in scenarios where new classes may consistently emerge. It is also well suited for applications requiring precise recognition, especially in discriminating between closely related classes. Furthermore, the DML exhibits greater resilience to issues related to class imbalance, as it concentrates on learning distances or similarities, which are more tolerant to such imbalances. These contributions significantly make the effectiveness and practicality of DML model, particularly in in fieldwork research.

SAM C-GAN: a method for removal of face masks from masked faces

The past years of COVID-19 have attracted researchers to carry out benchmark work in face mask detection. However, the existing work does not focus on the problem of reconstructing the face area behind the mask and completing the face that can be used for face recognition. In order to address this problem, in this work we have proposed a spatial attention module-based conditional generative adversarial network method that can generate plausible images of faces without masks by removing the face masks from the face region. The method proposed in this work utilizes a self-created dataset consisting of faces with three types of face masks for training and testing purposes. With the proposed method, an SSIM value of 0.91231 which is 3.89% higher and a PSNR value of 30.9879 which is 3.17% higher has been obtained as compared to the vanilla C-GAN method.

An Optimization-Based Technology Applied for Face Skin Symptom Detection

Face recognition segmentation is very important for symptom detection, especially in the case of complex image backgrounds or noise. The complexity of the photo background, the clarity of the facial expressions, or the interference of other people's faces can increase the difficulty of detection. Therefore, in this paper, we have proposed a method to combine mask region-based convolutional neural networks (Mask R-CNN) with you only look once version 4 (YOLOv4) to identify facial symptoms by this new method. We use the face image dataset from the public image databases DermNet and Freepic as the training source for the model. Face segmentation was first applied with Mask R-CNN. Then the images were imported into ResNet-101, and the facial features were fused with region of interest (RoI) in the feature pyramid networks (FPN) structures. After removing the non-face features and noise, the face region has been accurately obtained. Next, the recognized face area and RoI data were used to identify facial symptoms (acne, freckle, and wrinkles) with YOLOv4. Finally, we use Mask R-CNN, and you only look once version 3 (YOLOv3) and YOLOv4 are matched to perform the performance analysis. Although, the facial images with symptoms are relatively few. We still use a limited amount of data to train the model. The experimental results show that our proposed method still achieves 57.73%, 60.38%, and 59.75% of mean average precision (mAP) for different amounts of data. Compared with other methods, the mAP was more than about 3%. Consequently, using the method proposed in this paper, facial symptoms can be effectively and accurately identified.

PIS-YOLO: Real-Time Detection for Medical Mask Specification in an Edge Device

Wearing masks in a crowded environment can reduce the risk of infection; however, wearing nonstandard cloud does not have a good protective effect on the virus, which makes it necessary to monitor the wearing of masks in real time. You only look once (YOLO) series models are widely used in various edge devices. The existing YOLOv5s method meets the requirements of inference time, but it is slightly deficient in terms of accuracy due to its generality. Considering the characteristics of our driver medical mask dataset, a position insensitive loss which is cloud extract shared area feature in different categories and half deformable convolution net methods with cloud concern noteworthy features were introduced into YOLOv5s to improve accuracy, with an increase of 6.7% mean average in @.5 (mAP@.5) and 8.3% in mAP@.5:.95 for our dataset. To ensure that our method can be applied in a real scenario, TensorRT and CUDA were introduced to reduce the inference time in two edge devices (Jetson TX2 and Jetson Nano) and one desktop device, whose inference time was faster than that of previous methods.

Face Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism

Face mask-wearing detection is of great significance for safety protection during the epidemic. Aiming at the problem of low detection accuracy due to the problems of occlusion, complex illumination, and density in mask-wearing detection, this paper proposes a neural network model based on the loss function and attention mechanism for mask-wearing detection in complex environments. Based on YOLOv5s, we first introduce an attention mechanism in the feature fusion process to improve feature utilization, study the effect of different attention mechanisms (CBAM, SE, and CA) on improving deep network models, and then explore the influence of different bounding box loss functions (GIoU, CIoU, and DIoU) on mask-wearing recognition. CIoU is used as the frame regression loss function to improve the positioning accuracy. By collecting 7,958 mask-wearing images and a large number of images of people without masks as a dataset and using YOLOv5s as the benchmark model, the mAP of the model proposed in the paper reached 90.96% on the validation set, which is significantly better than the traditional deep learning method. Mask-wearing detection is carried out in a real environment, and the experimental results of the proposed method can meet the daily detection requirements.

SMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic

BACKGROUND AND OBJECTIVE

At present, the COVID-19 epidemic is still spreading worldwide and wearing a mask in public areas is an effective way to prevent the spread of the respiratory virus. Although there are many deep learning methods used for detecting the face masks, there are few lightweight detectors having a good effect on small or medium-size face masks detection in the complicated environments.

METHODS

In this work we propose an efficient and lightweight detection method based on YOLOv4-tiny, and a face mask detection and monitoring system for mask wearing status. Two feasible improvement strategies, network structure optimization and K-means++ clustering algorithm, are utilized for improving the detection accuracy on the premise of ensuring the real-time face masks recognition. Particularly, the improved residual module and cross fusion module are designed to aim at extracting the features of small or medium-size targets effectively. Moreover, the enhanced dual attention mechanism and the improved spatial pyramid pooling module are employed for merging sufficiently the deep and shallow semantic information and expanding the receptive field. Afterwards, the detection accuracy is compensated through the combination of activation functions. Finally, the depthwise separable convolution module is used to reduce the quantity of parameters and improve the detection efficiency. Our proposed detector is evaluated on a public face mask dataset, and an ablation experiment is also provided to verify the effectiveness of our proposed model, which is compared with the state-of-the-art (SOTA) models as well.

RESULTS

Our proposed detector increases the AP (average precision) values in each category of the public face mask dataset compared with the original YOLOv4-tiny. The mAP (mean average precision) is improved by 4.56% and the speed reaches 92.81 FPS. Meanwhile, the quantity of parameters and the FLOPs (floating-point operations) are reduced by 1/3, 16.48%, respectively.

CONCLUSIONS

The proposed detector achieves better overall detection performance compared with other SOTA detectors for real-time mask detection, demonstrated the superiority with both theoretical value and practical significance. The developed system also brings greater flexibility to the application of face mask detection in hospitals, campuses, communities, etc.

ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

During the last two years, several deep learning-based methods for face mask detection have been proposed by researchers. However, most of the proposed methods struggle with the detection of face masks that are too small an object to detect and further achieve low detection accuracy. Considering the issues of the existing methods, in this work, we have proposed ETL-YOLO v4 with a modified and improved feature extraction and prediction network for tiny YOLO v4 which surpasses all its predecessors and other related work in the literature. To develop ETL-YOLO v4, we have improved the backbone architecture of tiny YOLO v4 by adding a modified-dense SPP network, two additional detection layers with modified and optimized CNN layers that aid in accurate prediction, used Mish as the activation function, and utilized modified anchor boxes. Furthermore, to obtain detection results in images of varied viewpoints, we have added Mosaic and CutMix data augmentation at training time. The proposed ETL-YOLO v4 achieved 9.93% higher mAP, 5.75% higher average precision (AP) for faces with masks, and 16.6% higher average precision (AP) for the face mask region as compared to its original base-line variant.

An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4

BACKGROUND

Circulating tumor cells (CTCs) acting as "liquid biopsy" of cancer are cells that have been shed from the primary tumor, which cause the development of a secondary tumor in a distant organ site, leading to cancer metastasis. Recent research suggests that CTCs with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood samples, namely circulating genetically abnormal cells (CACs), could be used as a non-invasive decision tool to detect patients with benign pulmonary nodules. Such cells are identified by counting the fluorescence signals of fluorescence in situ hybridization (FISH). However, owing to the rarity of CACs in the blood, identification of CACs using this technique is time-consuming and is a drawback of this method.

METHODS

This study has proposed an efficient and automatic FISH-based CACs identification approach which is based on a combination of the high accuracy of You Only Look Once (YOLO)-V4 and the lightweight and rapidness of MobileNet-V3. The backbone of YOLO-V4 was replaced with MobileNet-V3 to improve the detection efficiency and prevent overfitting, and the architecture of YOLO-V4 was optimized by utilizing a new feature map with a larger scale to enable the enhanced detection ability for small targets.

RESULTS

We trained and tested the proposed model using a dataset containing more than 7,000 cells based on five-fold cross-validation. All the images in the dataset were 2,448×2,048 (pixels) in size. The number of cells in each image was >70. The accuracy of four-color fluorescence signals detection for our proposed model were all approximately 98%, and the mean average precision (mAP) were close to 100%. The final outcome of the developed method was the type of cells, i.e., normal cells, CACs, gaining cells or deletion cells. The method had a CACs identification accuracy of 93.86% (similar to an expert pathologist), and a detection speed that was about 500 times greater than that of a pathologist.

CONCLUSIONS

The developed method could greatly increase the review accuracy, enhance the efficiency of reviewers, and reduce the review turnaround time during CACs identification.

Fast and Robust People Detection in RGB Images

People detection in images has many uses today, ranging from face detection algorithms used by social networks to help the users tag other people, to surveillance systems that can create a statistic of the population density in an area, or identify a suspect, or even in the automotive industry as part of the Pedestrian Crash Avoidance Mitigation (PCAM) system. This work focuses on creating a fast and reliable object detection algorithm that will be trained on scenes that depict people in an indoor environment, starting from an existing state-of-the-art approach. The proposed method improves upon the You Only Look Once version 4 (YOLOv4) network by adding a region of interest classification and regression branch such as Faster R-CNN’s head. The candidate bounding boxes proposed by YOLOv4 are ranked based on their confidence score, the best candidates being kept and sent as input to the Faster Region-Based Convolutional Neural Network (R-CNN) head. To keep only the best detections, non-maximum suppression is applied to all proposals. This decreases the number of false-positive candidate bounding boxes, the low-confidence detections of the regression and classification branch being eliminated by the detections of YOLOv4 and vice versa in the non-maximum suppression step. This method can be used as the object detection algorithm in an image-based people tracking system, namely Tracktor, having a higher inference speed than Faster R-CNN. Our proposed method manages to achieve an overall accuracy of 95% and an inference time of 22 ms.