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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland

Email: pawel.olejnik@p.lodz.pl

C. Steve Suh (editor)

Texas A&M University, USA

Email: ssuh@tamu.edu

Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China

Email: tuoxianguo@suse.edu.cn


Wear Detection Method of Electric Power Field Safety Appliances based on Deep Learning

Journal of Vibration Testing and System Dynamics 8(1) (2024) 67--76 | DOI:10.5890/JVTSD.2024.03.005

Zhu Shi, Hao Wu, Zhong-yang Jin, Hong Song

School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin, China

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Abstract

Aiming at the problem of low detection accuracy of small and medium targets and occluded targets in power field, a new wear detection method improved SBC\_YOLOv5 is proposed. This method is based on expansion convolution, and uses multi-scale pooling operation and MHSA self attention module to improve the spatial pyramid pooling layer, so that it can obtain more abundant receptive fields. Secondly, it uses feature bridging operation and CARAFE operator to improve the Neck network, so as to improve the network's ability to extract and compensate semantic features. Finally, it uses CIoU to optimize the network's loss function and improve the regression ability of the model. The experimental results show that the wear detection method SBC established in this paper\_ The average accuracy mAP(Mean Average Precision) of YOLOv5 is 82.3\%, the recall rate is 81.5\%, and the detection speed FPS is 44. Compared with original YOLOv5, YOLOv4, and Faster RCNN, the mAP values of YOLOv5 are increased by 1.5\%, 10.27\%, and 25.21\%, respectively. It can effectively improve the detection accuracy of small targets and occluded targets, and meet the real-time and accuracy requirements of wear detection at power operation sites.

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