基于Yolo v5与Grabcut的架空线路绝缘子可见光图像自动识别与分割方法

Visible Light Image Automatic Recognition and Segmentation Method for Overhead Power Line Insulators Based on Yolo v5 and Grabcut

准确识别与分割绝缘子图像是架空线路绝缘子状态感知与缺陷诊断的重要前提。针对绝缘子的可见光图像提出了结合深度学习Yolo(You only look once)v5模型与图论分割Grabcut算法的两阶段图像识别与分割方法。首先采集图像建立数据集并训练Yolo v5,实现绝缘子的识别,然后用识别框坐标确定感兴趣区域,预判前景和背景,基于Grabcut算法实现绝缘子的自适应分割。根据该方法识别与分割架空线路绝缘子的在线监测可见光图像,结果表明,该方法无需分割标注或人工交互,可以高效准确地定位复杂背景下绝缘子的边缘并分割绝缘子,极大地提高绝缘子图像的分析效率。

Accurate recognition and segmentation of insulator image is an important prerequisite for state perception and defect diagnosis of overhead power line insulators.In this paper,a two-stage image recognition and segmentation method combining the Yolo(you only look once)v5 and Grabcut is proposed for the visible image of insulators.This paper firstly collects images,establishes data set and trains Yolo v5 to realize insulator recognition.Then the recognition frame coordinates are used to determine the region of interest,predict the foreground and background,and realize the adaptive segmentation of insulators based on the Grabcut method.This method is used to identify and segment the on-line monitoring visible light image of overhead power line insulators.The results show that this method can accurately locate the edge of insulator under complex background and segment insulator without segmentation and annotation or manual interaction,which can efficiently improve the efficiency of insulator image analysis.