Insulator defect detection algorithm based on improved YOLOv5s

Abstract

As one of the extremely important components on the transmission tower, the insulator has two functions of electrical insulation and wire fixing, which directly affects the operation of the power system. Defects in insulators can impair the service life of transmission lines. UAV aerial photography of electric power towers has problems such as small number of defective insulator samples, small area, large aspect ratio of insulator strings, and variable inclination angle, coupled with the influence of environmental factors such as light, interference, distance, etc., which lead to low detection accuracy of insulator defects. Aiming at the above problems, an improved YOLOv5 insulator defect detection algorithm is proposed. First, screen the aerial images and use data augmentation to obtain a sufficient number of defective insulator images to enrich the dataset and avoid model overfitting. Secondly, the convolutional attention module CBAM is introduced to improve the expression ability of defect insulator features and strengthen the network's ability to identify targets. Finally, the Leaky ReLU activation function of the hidden layer of the original YOLOv5 algorithm is replaced by the Mish function to improve the generalization ability of the network. The experimental results show that compared with the original YOLOv5 algorithm, the average precision mAP (IOU=0.5) of the improved algorithm is increased by 7.8%, which effectively improves the problems of false detection and missed detection in the original algorithm. Compared with other mainstream object detection algorithms, the algorithm proposed in this paper has better detection effect on insulator defects.