基于U-net和YOLOv4的绝缘子图像分割与缺陷检测  

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

作　　者：唐小煜[1] 黄进波 冯洁文 陈锡和 TANG Xiaoyu;HUANG Jinbo;FENG Jiewen;CHEN Xihe(Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials∥Guangdong Provincial Engineering Technology Research Center for Optoelectronic Instrument∥National Demonstration Center for Experimental Physics Education∥School of Physics and Telecommunication Engineering,South China Normal University,Guangzhou 510006,China)

机构地区：[1]广东省量子调控工程与材料重点实验室∥广东省光电检测仪器工程技术研究中心∥物理国家级实验教学示范中心∥华南师范大学物理与电信工程学院,广州510006

出　　处：《华南师范大学学报（自然科学版）》2020年第6期15-21,共7页Journal of South China Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(61371176);广州市高校创新创业教育项目(2019HD206)。

摘　　要：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.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.

关 键 词：绝缘子 语义分割 目标检测 U-net YOLOv4 

分 类 号：P407.8[天文地球—大气科学及气象学] TP75[自动化与计算机技术—检测技术与自动化装置]