Insulator defect detection algorithm based on a lightweight network

Abstract

Abstract Insulators are the key components of transmission lines. The identification and detection of insulator defects are directly related to the stable operation of transmission lines. In order to improve the efficiency of the insulator and its defect location, a faster defect detection algorithm based on YOLOv5 is proposed. Firstly, a lightweight Ghost module was introduced in the YOLOv5 backbone network, which significantly improved the detection speed with ensuring accuracy. Secondly, Secondly, CBAM is introduced into YOLOv5 Neck network to further improve the detection accuracy. The experimental results show that the model of the improved post-network is smaller compared to the YOLOv5 original network, and the detection speed improves greatly while ensuring the detection accuracy. It is of great significance to power grid operation and maintenance.