Research on the state detection of the secondary panel of the switchgear based on the YOLOv5 network model
Wang Chen, Zhang Liqiang, Tianpeng Yan, Jiang Tao, Jia Yijing, Zhihao Lai · Journal of Physics Conference Series · 2021
Abstract In recent years, with the development of artificial intelligence algorithms, deep learning algorithms are widely used in target detection. The switchgear in the power system plays a key role in the safe operation of the system and there are a large number of them. In order to improve the work efficiency of testers and reduce manual misjudgment, it is proposed to apply the deep learning YOLOv5 algorithm to detect the status of the switchgear secondary panel in real time. The algorithm uses the configuration environment, The data set training and target test obtain the light state information, and the experimental results prove the effectiveness of the algorithm, which provides technical support for the field of switchgear electrical test robots.