Lightweight Improved Transmission Line External Mechanical Damage Threats Detection Algorithm

被引:0
|
作者
Wang, Yanhai [1 ,2 ,3 ]
Guo, Chenxin [1 ,2 ]
Wu, Deqiang [1 ,2 ]
机构
[1] China Three Gorges Univ, Sch Elect & New Energy, Yichang 443002, Hubei, Peoples R China
[2] China Three Gorges Univ, Hubei Prov Engn Technol Res Ctr Power Transmiss Li, Yichang 443002, Hubei, Peoples R China
[3] China Three Gorges Univ, Key Lab Geol Hazards Three Gorges Reservoir Area, Minist Educ, Yichang 443002, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
target detection; external mechanical damage; YOLOv5s; lightweight improvement; transmission lines;
D O I
10.1002/tee.24163
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In monitoring transmission line external damage prevention, due to the limited memory computing power of the equipment, the image needs to be transmitted to the data center at regular intervals, resulting in a high false negative rate. Therefore, this paper proposes a target detection method based on lightweight YOLOv5s. First, DSConv and improved E-ELAN are used in Backbone to reduce the model's parameters. Then, GSConv and VoV-GSCSP are introduced in Neck to reduce the complexity of the model. Finally, the Mish activation function achieves more effective feature transfer. According to the experimental findings, the proposed model's parameters are about 37% smaller than the original model's, and the calculation amount is about 53% smaller. The detection accuracy on the self-built data set is the same, which proves that the proposed algorithm can reduce the model while maintaining high detection performance. It has specific practical significance for the terminal real-time detection of external mechanical damage targets. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
引用
收藏
页码:2002 / 2011
页数:10
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