Method of insulator defect detection based on the improved YOLOv5s

Abstract

Aiming at the problem of low accuracy in detecting small defects in insulator aerial images using the current YOLOv5s target detection network, this paper designs an insulator defect detection algorithm based on improved YOLOv5s. Firstly, in the feature extraction stage, a PST attention module is embedded to suppress redundant interfering feature information; The guidance network grasps effective features for learning during the training process. Secondly, a feature fusion module with a bilateral structure is used in the feature fusion process, making full use of contextual semantic information to improve the accuracy of small-scale target detection. The experimental results show that the average accuracy of the improved YOLOv5 algorithm is 81.82%, which is 4.71 percentage points higher than YOLOv5s algorithm and 1.2 percentage points higher than YOLOX-S. This indicates that the improved YOLOv5s network model used in this paper has good results in the detection of insulator defects and has certain application value in actual power inspection tasks.