An improved YOLOv5 algorithm for fault diagnosis of infrared transmission line images

With the rapid development of society, our demand for electricity is increasing. We need to lay more power transmission cables. Transmission lines will undergo various harsh environmental tests and encounter various problems during operation. The occurrence of transmission line faults can cause serious hidden dangers to the power grid, and the safety issues of transmission lines have become increasingly serious. To effectively solve this problem, we need to quickly detect faults in transmission lines to ensure the safe operation of the power transportation system. However, traditional infrared fault detection methods are mainly based on manual detection and are now difficult to meet the development needs of today's era. Therefore, this article proposes a deep learning based fault target detection algorithm for transmission lines. We can effectively achieve automatic identification of transmission line faults through an improved Yolov5-TL algorithm.