架空输电线路无人机巡检影像中的隐患识别  

Hidden danger identification in UAV inspection images of overhead transmission lines

作　　者：刘高 陈浩 廖建东 周华敏 饶成成 LIU Gao;CHEN Hao;LIAO Jiandong;ZHOU Huamin;RAO Chengcheng(School of Electrical Engineering,China Three Gorges University,Yichang 443002,Hubei,China;Machine Patrol Management Center,Guangdong Power Grid Co.,Ltd.,Guangzhou 510699,Guangdong,China)

机构地区：[1]三峡大学电气学院,湖北宜昌443002 [2]广东电网有限责任公司机巡管理中心,广东广州510699

出　　处：《沈阳工业大学学报》2025年第2期258-264,共7页Journal of Shenyang University of Technology

基　　金：国家重点研发计划项目(2021YEE0204200);广东电网有限责任公司科技项目(031000KK52200004)。

摘　　要：【目的】在电力系统中,架空输电线路作为电能传输的关键环节,其安全稳定运行尤为重要。然而,随着自然环境变化和植被快速生长,输电线路走廊内的树木成为影响线路安全的主要隐患之一。树木与输电线路的接近度过高不仅可能引发短路、跳闸等故障,严重时还会导致火灾,威胁电网安全及人民生命财产。因此,为提高树障隐患识别的准确性,设计了一种基于无人机(UAV)巡检影像的架空输电线路树障隐患识别方法。【方法】首先,通过直方图均衡化增强影像对比度,使细节信息更为清晰,并采用变换函数强化影像边缘,为特征提取奠定基础。然后,利用FROST滤波去除影像噪声,在保留边缘细节的同时确保处理准确性。结合二值化方法平滑影像,提取巡检影像中的树障颜色特征及输电线路导线弧垂的纹理特征。针对影像拍摄角度和光线导致的边缘信息缺失,采用插值算法补充缺失的边缘值,保证特征提取的完整性。在此基础上,通过计算相邻数据的欧氏距离获得特征融合的标注结果,实现对输电线路走廊隐患的识别。【结果】实验结果显示,本文方法在识别架空输电线路树障隐患任务中表现优异,不仅准确识别出5个树障隐患区域,且隐患数量识别结果与实际误差较小。在隐患位置坐标的精确性分析中,b区域和d区域的识别坐标分别为(1.43 m,8.3 m)和(1.49 m,9.8 m),与实际数据高度吻合。此外,相较于其他方法的隐患程度识别不准确情况,本文方法在各级树障隐患方面的识别结果更为精确,与实验区域实际情况的数值接近,验证了方法实际应用优势和可靠性。【结论】本文方法能够有效识别输电线路的隐患区域,精准判断隐患数量及特征,具有较高的实用性。该方法结合无人机巡检影像与先进图像处理技术,实现了架空输电线路树障隐患的自动化、智能化识别。通过融合颜�[Objective]In the power system,overhead transmission lines are a critical link in the transmission of electrical energy,and their safe and stable operation is crucial.However,with the continuous changes in the natural environment and rapid growth of vegetation,trees in transmission line corridors have become one of the main hidden dangers affecting line safety.The high proximity between trees and transmission lines may not only cause faults such as short circuits and tripping but also lead to fires in severe cases,posing a serious threat to the safety of the power grid and people′s lives and property.Therefore,to improve the accuracy of identifying tree obstacles,this paper designed a method to identify tree obstacles for overhead transmission lines based on unmanned aerial vehicle(UAV)inspection images.[Methods]To improve the quality of UAV inspection images,histogram equalization was used to enhance the contrast of the images,making the detailed information in the images clearer.The use of transformation functions further enhances the edge features of the images,laying the foundation for subsequent feature extraction.The FROST filter was used to remove image noise,ensuring accuracy of subsequent processing while preserving edge details.The images were smoothed using binarization methods,and the color features of tree obstacles and the texture features of conductor sag of the transmission line were extracted from the inspection images.In response to the missing edge information in images due to factors such as shooting angle and lighting,an interpolation algorithm was used to supplement the missing image edge values,ensuring the integrity of feature extraction.On this basis,the Euclidean distance between adjacent data was calculated to obtain the annotation results of feature fusion.Consequently,hidden dangers in overhead transmission line corridors were identified.[Results]The experimental results show that the proposed method performs well in the task of identifying hidden dangers brought by tree obstacles f

关 键 词：输电线路走廊 隐患识别 二值化方法 直方图均衡化 特征提取 欧氏距离 无人机巡检 特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]