An Insulator Burst Defect Detection Method Based on YOLOv4-MP

Aiming at the problems of small defect targets and complex backgrounds, which leads to low accuracy in insulator burst defect detection, an improved detection algorithm YOLOv4-MP based on YOLOv4 is proposed. First, the Shuffle Attention module is included in the feature extraction network to limit the interference of complicated background, enabling the model to extract more effective feature information. Then, the spatial pyramid pooling is enhanced by the dilated pooling layer, which effectively increases the receptive field and improves the effect of feature fusion. Finally, the Mish function is utilized as the activation function of the path enhancement network to limit the loss of low-level information. The experimental results show that the mean average precision of YOLOv4-MP reaches 93.60%, which is 6.37% higher than that of the YOLOv4 algorithm. Compared with the commonly used detection algorithms, YOLOv4-MP has better detection performance and has high application value for the detection of insulator burst defects.