Aiming at the actual problems of low detection accuracy and low detection speed in the detection of power insulators and insulator defects in a complex environment background, an improved you only Look once v4(YOLOv4) algorithm for power insulator images and existence Method of detecting defective insulators is proposed. By making a dataset of power insulators and insulators with defects, using K-means clustering (K-means) algorithm to cluster the power insulator image samples to obtain different sizes of a priori box parameters; then by improving the balance cross entropy (Balanced Cross Entropy, BCE), it introduces a weight coefficient to increase the contribution of the loss function. Finally, the depth of the network is deepened by adding convolutional layers before and after the spatial pyramid pooling ( SPP) structure. The experimental results show that the single sheet detection time of the improved model is 3. 27 s, and the average detection accuracy of insulator defects is improved by 24. 36% compared with the original YOLOv4 algorithm. At the same time, through the improved YOLOv4 algorithm, the value of mean average precision(mAP) on the test set is 84. 05%, which is 17. 83% higher than the original YOLOv4 algorithm, which fully demonstrates the ability to locate and identify of the defect in power insulator images well.