Image Segmentation and Defect Detection of Insulators Based on U-net and YOLOv4

The integrity of the insulator on transmission lines directly affects the safety and reliability of power transmission. Deep learning methods are used to explore the problems of insulator image extraction and defect detection. First, the mask image of the insulator region is obtained with the optimized U-net model so as to implement the semantic segmentation of the insulator. Then the YOLOv4 model is built to obtain the position of the defective insulator and achieve the detection of the self-explosive insulator. Aiming at making full use of the pixel information of high-resolution images, the "segmentation-recognition-synthesis" model is proposed to segment the insulator and obtain the defect area precisely. Multiple groups of experimental comparison are designed to verify the effectiveness of the model. The Dice coefficient of insulator segmentation based on the optimized U-net model is up to 0.92; the accuracy of the self-explosive insulator identification based on the YOLOv4 model is up to 0.96, and the average degree of overlapping IOU reaches 0.88. The research results have high practical value for the realization of intelligent power system operation and maintenance.