Aiming at the problem of current slow detection speed of insulator defects in high-voltage lines and low accuracy in complex scenarios,this paper proposes an insulator defect detection algorithm for complex scenes based on lightweight you only look once (YOLOv4).Firstly,the lightweight efficient channel attention GhostNet (ECA-GhostNet) is used as the backbone to improve the detection speed.Then the classification-IoU joint representation is introduced in the head,and the general distribution is utilized to represent the flexible distribution of the bounding boxes to improve detection performance in complex scenes.In the training phase,quality focal loss (QFL) and distribution focal Loss (DFL) are used to better supervise joint representation and bounding boxes regression.Proposed method verifies the two types of targets of normal and self-explosive defective insulators on a dataset with complex background.The results shows that the detection accuracy of our approach in complex scenes is better than the current mainstream algorithms,and the detection speed reaches 49 FPS,which is about 40% higher than the original YOLOv4 algorithm′s detection speed.