基于U-net和YOLOv4的绝缘子图像分割与缺陷检测

输电线路上绝缘子的完整性直接影响了输电的安全与可靠性.采用深度学习方法，对绝缘子图像识别提取和缺陷检测问题进行了研究.首先基于优化的U-net模型获取绝缘子区域掩模图像，实现对绝缘子串语义分割；然后基于YOLOv4模型获取缺陷绝缘子的位置，实现对自爆绝缘子目标的检测.为充分利用高分辨率图像的像素信息，提出“切分-识别-合成”的检测思路，精确分割出绝缘子以及判断并获取缺陷区域；最后设计了多组实验并进行对比，验证了模型的有效性.采用优化的U-net模型分割绝缘子的Dice系数达0.92；采用YOLOv4模型检测自爆绝缘子的识别精度达0.96，平均重叠度IOU达0.88.研究结果对实现电力系统运维的智能化具有较高的应用价值.

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.