基于YOLOv4和改进分水岭算法的绝缘子爆裂检测定位研究

Research on Insulator Burst Fault Identification Based on YOLOv4 and Improved Watershed Algorithm

近年来,航拍巡检代替人工成为了输电线路电力巡检的主要方式,而输电线路上绝缘子的完整性直接影响其供电可靠性。在复杂背景的干扰下,传统的图片处理方法往往对主体识别能力低下。针对这一问题,该文提出了一种基于YOLOv4的深度学习并结合改进的分水岭算法,对航拍绝缘子图像精确识别及缺陷检测的问题进行了研究。首先利用YOLOv4对绝缘子进行精准的识别与定位,有效弥补了传统方法在复杂背景下识别能力低下的不足;再结合改进分水岭算法对绝缘子自爆位置进行识别,该方法可以快速地识别出绝缘子主体和缺陷位置。

In recent years,aerial inspection instead of manual inspection has become the main method of power inspection of transmission lines,and the integrity of the insulators on the transmission line directly affects the reliability of its power supply.Traditional image processing methods often have low ability to recognize subjects under the interference of complex backgrounds.In response to this problem,this paper proposes a deep learning method based on YOLOv4 combined with an improved watershed algorithm to study the precise identification and defect detection of aerial insulator images.First,YOLOv4 is used to accurately identify and locate the insulators,which effectively compensates for the poor recognition ability of traditional methods in complex backgrounds.Second,combined with the improved watershed algorithm the location of the insulator self-detonation is identified.This method can quickly identify the main body of the insulator and the location of the defect.