Improved glove defect detection algorithm based on YOLOv5 framework

Wang, Huibai; Wang, Yuxuan

doi:10.1109/iaeac54830.2022.9929876

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…Attention mechanism [35] which simulates the human brain to focus on the importance of information, has been known to be an effective approach to advance model performance. Huang et al [36] added CBAM to YOLOv5 framework's backbone network and bidirectional feature pyramid network (BIFPN) to the neck network and improved the BCs detection accuracy by 89.9%. Meanwhile, Wencheng Gu et al [37] introduced Transformer [35] encoder block and CBAM attention into YOLOv5 frameworks.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Blood Cell Detection Based on Advanced YOLOv5s Network

2024

IEEE Access

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There is a great demand for automatic detection and classification of blood cells (BCs) in clinical medical diagnoses. Traditional methods, such as hematology analyzer and manual counting were laborious, time intensive, and limited by analysts' professional experience and knowledge. In this paper, the one-stage network based upon improved YOLOv5s is provided to detect BCs. First, the Transformer and bidirectional feature pyramid network (BiFPN) are introduced into the backbone network and neck network for refining the adaptive features, respectively. Second, Convolutional Block Attention Module (CBAM) is added to neck network outputs to strengthen the key features in space and channel. In addition, an Efficient Intersection over Union (EIoU) was introduced to improve model accuracy regarding localization and performance. The improvements are embedded into the YOLOv5s model and termed YOLOv5s-TRBC. The experiments on the blood cell dataset (BCCD) show that in the three types of BCs detections, the mean average precision (mAP) of the method proposed reached 93.5%. Furthermore, comparative experiments demonstrate that the model could perform favorably against the counterparts with respect to mAP rate, and the model's Giga Floating-point Operations Per Second (GFLOPs) is reduced to 1/6 of YOLOv5, which provides a potential solution for future computer-aid diagnostic systems.

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Section: Related Workmentioning

confidence: 99%

“…Faster R-CNN and CenterNet are classical models for object detection and classification. Also, the proposed model was compared with some latest methods listed in the literature [52,53,24,54,36,37].…”

Section: Detection Performance Comparisonmentioning

confidence: 99%

Automatic Blood Cell Detection Based on Advanced YOLOv5s Network

2024

IEEE Access

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“…The current research on human target detection is mainly to improve the one-stage target detection algorithm model. For example, Wang [6] and others proposed a lightweight human detection algorithm based on YOLOv5 to solve the problems of large human recognition algorithm model and slow detection speed. In this paper, GhostNet is used to reconstruct the YOLOv5 network, and the two layers of the backbone network are deleted to reduce the amount of network parameters.…”

Section: Introductionmentioning

confidence: 99%

Human target detection method based on MobileNet-SSD combined with frame difference method

Ma,

Yu,

et al. 2024

International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024)

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“…Head is the prediction part of the network, and output three groups of vectors containing the category of the prediction box, confidence and coordinate position [10] .…”

Section: Introduction To Yolov5 Algorithmmentioning

confidence: 99%

Research on steel surface defect detection based on YOLOv5

Zhang¹,

Chen²,

Qi³

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Aiming at the problems of low detection efficiency and poor detection accuracy of traditional steel surface defect detection methods, this paper proposes a steel surface defect detection algorithm based on improved YOLOv5 network. First, the lightweight GhostNet network is introduced, and the Ghost module is used to optimize the YOLOv5 backbone feature extraction network to obtain a lightweight model to reduce the complexity of the model and improve the speed of crack detection; Then, the efficient CA (Coordinate Attention) channel attention mechanism is fused at the model prediction output to further enhance the defect feature extraction ability and improve the model detection performance. The simulation results show that the model volume of this method is reduced by 10.3% and the mAP value is increased by 4.7% compared with the existing YOLOv5 algorithm. Compared with traditional steel surface defect detection methods, the proposed algorithm can detect the types and locations of steel surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals.

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Improved glove defect detection algorithm based on YOLOv5 framework

Cited by 5 publications

References 7 publications

Automatic Blood Cell Detection Based on Advanced YOLOv5s Network

Automatic Blood Cell Detection Based on Advanced YOLOv5s Network

Human target detection method based on MobileNet-SSD combined with frame difference method

Research on steel surface defect detection based on YOLOv5

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