Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5

Guo, Shuyi; Li, Lulu; Guo, Tianyou; Cao, Yulian; Li, Yinlei

doi:10.3390/s22134933

Cited by 37 publications

(14 citation statements)

References 36 publications

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“…The coordinate attention is introduced into the feature fusion module in the YOLOv5 algorithm to improve the network’s attention to the object of interest and reduce the weight of irrelevant factors in the image to improve the model detection performance, combined with the study of coordinate attention insertion positions by Guo et al [ 41 ] In order to mitigate the overfitting phenomenon arising from insufficient information generalization by the model’s channel weights and spatial weights, the insertion positions have been stratified into three distinct groups for comprehensive examination in this study. Figure 7 illustrates the insertion positions, while Table 1 presents the experimental results.…”

Section: Methodsmentioning

confidence: 99%

Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention

Gao,

Ma,

Zhao

et al. 2023

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention

Gao,

Ma,

Zhao

et al. 2023

Sensors

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“…(3) Utilize only weakly supervised signals for detection to reduce the workload of data annotation, thus improving the model's Robustness. [10] The redundancy effect on features after fusion is significantly reduced by adding a coordinate attention mechanism to emphasize key features. The feature pyramid structure in feature fusion is replaced with a weighted bidirectional feature pyramid, which achieves funny bidirectional cross-scale connectivity and weighted feature fusion.…”

Section: Commonly Used Deep Learning Modelsmentioning

confidence: 99%

A Review of the Application of Artificial Intelligence in Orthopedic Diseases

Diao,

Wang,

Qin

et al. 2024

CMC

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In recent years, Artificial Intelligence (AI) has revolutionized people's lives. AI has long made breakthrough progress in the field of surgery. However, the research on the application of AI in orthopedics is still in the exploratory stage. The paper first introduces the background of AI and orthopedic diseases, addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases, draws out the advantages of deep learning and machine learning in image detection, and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years, describing the contributions, strengths and weaknesses, and the direction of the future improvements that can be made in each study. Next, the paper also introduces the difficulties of traditional orthopedic surgery and the roles played by AI in preoperative, intraoperative, and postoperative orthopedic surgery, scientifically discussing the advantages and prospects of AI in orthopedic surgery. Finally, the article discusses the limitations of current research and technology in clinical applications, proposes solutions to the problems, and summarizes and outlines possible future research directions. The main objective of this review is to inform future research and development of AI in orthopedics.

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“…The experimental data suggest better results when the Convolutional Block Attention Module (CBAM) is inserted before YOLOv4's three heads and within the neck network. Guo et al [32] proposed the YOLOv5-CBD model, fusing a coordinated attention mechanism and a weighted bidirectional feature pyramid network for the purpose of bolstering detection accuracy. However, this model focuses on the recognition problem of the mask itself.…”

Section: Related Workmentioning

confidence: 99%

A Mask-Wearing Detection Model in Complex Scenarios Based on YOLOv7-CPCSDSA

Wang

Zhang

et al. 2023

Electronics

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With the rapid development of deep learning technology, many algorithms for mask-wearing detection have achieved remarkable results. However, the detection effect still needs to be improved when dealing with mask-wearing in some complex scenes where the targets are too dense or partially occluded. This paper proposes a new mask-wearing detection model: YOLOv7-CPCSDSA. Based on YOLOv7, this model replaces some convolutions of the original model, CatConv, with FasterNet’s partial convolution (PConv) to form a CatPConv (CPC) structure, which can reduce computational redundancy and memory access. In the case of an increase in the network layer, the parameters are reduced instead. The Small Detection (SD) module is added to the model, which includes structures such as upsampling, concat convolution, and MaxPooling to enhance the ability to capture small targets, thereby improving detection accuracy. In addition, the Shuffle Attention (SA) mechanism is introduced, which enables the model to adaptively focus on important local information, thereby improving the accuracy of detecting mask-wearing. This paper uses comparative and ablation experiments in the mask dataset (including many images in complex scenarios) to verify the model’s effectiveness. The results show that the mean average precision@0.5 (mAP@0.5) of YOLOv7-CPCSDSA reaches 88.4%, which is 1.9% higher than that of YOLOv7, and its frames per second (FPS) rate reaches 75.8 f/s, meeting the real-time detection requirements. Therefore, YOLOv7-CPCSDSA is suitable for detecting mask-wearing in complex scenarios.

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Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5

Cited by 37 publications

References 36 publications

Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention

Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention

A Review of the Application of Artificial Intelligence in Orthopedic Diseases

A Mask-Wearing Detection Model in Complex Scenarios Based on YOLOv7-CPCSDSA

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