• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2023, Vol. 44 ›› Issue (1): 91-99.doi: 10.12204/j.issn.1000-7229.2023.01.011

• Smart Grid • Previous Articles     Next Articles

Real-Time Detection Method for Transmission Line Faults Applying Edge Computing and Improved YOLOv5s Algorithm

HUANG Yuehua(), CHEN Zhaoyuan(), CHEN Qing(), ZHANG Lei(), LIU Hengchong(), ZHANG Jiarui()   

  1. College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,Hubei Province,China
  • Received:2022-04-08 Online:2023-01-01 Published:2022-12-26
  • Contact: CHEN Qing E-mail:hyh@ctgu.edu.cn;897201539@qq.com;chenqing20190808@163.com;leizhang3188@163.com;2162015559@qq.com;892676757@qq.com


With the normalization of unmanned aerial vehicle (UAV) inspection of transmission lines, the problems of real-time detection of fault images and accuracy of blurred target detection are difficult to meet the actual work requirements. This paper proposes a real-time detection method for transmission line faults, which is based on edge computing and improved YOLOv5s algorithm. This method is based on YOLOv5s model, and the model is reconstructed on the basis of Ghost lightweight module to realize the convolution operation process of obtaining data features, which improves the detection speed of the model. The loss function based on KL (Kullback-Leibler) divergence distribution is used as the target box localization loss function to improve the accuracy of blurred image detection. The improved YOLOv5s algorithm is deployed in the Huawei Atlas 200 DK edge module to detect three types of faults: insulator self-explosion, shock hammer falling-off, and bird’s nest. The mAP can reach 84.75%, and the detection speed is 34 frame/s. The results show that the improved algorithm can improve the detection accuracy of blurred fault target images while ensuring the real-time detection, and meet the inspection requirements of transmission lines equipped with edge devices by UAV.

This work is supported by National Natural Science Foundation of China (No. 52007103) and Major Science and Technology Projects in Hubei Province of China (No. 2020AEA012).

Key words: edge computing, transmission line fault, YOLOv5s, real-time detection, Ghsot lightweight module, KL divergence

CLC Number: