Lightweight Improved Transmission Line External Mechanical Damage Threats Detection Algorithm

Abstract

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. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.