摘要
针对现有输电线路检测存在的缺陷类别少、尚无统一检测参数等问题,文章提出一种基于深度学习的输电线路缺陷检测方法。首先,建立基于Wire_10的传输线路缺陷识别数据集;为进一步降低因背景和照明因素对识别准确度的影响,定义相关数据集,提出一种基于R-CNN的端到端高识别精度的深度学习算法,用于建立具有转移学习和微调的检测模型。最后,实验结果表明,该检测方法能够准确识别Wire_10数据集中的缺陷类别,并对具有复杂背景和不同照明的航空图像具有鲁棒性。
To solve the problems of few detection categories and lack of unified comprehensive evaluation index in the existing transmission line detection,this paper proposes a deep learning-based transmission line defect detection and evaluation method.Defects are treated as a category by building a Wire_10 based transmission line dataset.In order to further reduce the target detection of images due to the attachment of nests and foreign objects in the line,which is easily affected by the background and lighting,these two factors are used as variables to define the background dataset and lighting dataset,and a new method based on R-CNN end-to-end high recognition accuracy deep learning algorithm is proposed for building detection models with transfer learning and fine-tuning.The results show that the detection method can accurately identify defect categories in the Wire_10 dataset and is robust to aerial images with complex backgrounds and different illuminations.
作者
李旭旭
马小敏
唐军
孔庆宇
于军亮
LI Xuxu;MA Xiaomin;TANG Jun;KONG Qingyu;YU Junliang(State Grid Sichuan Electric Power Company,Chengdu 610095,Sichuan Province,China;Electric Power Research Institute,State Grid Sichuan Electric Power Company,Chengdu 610095,Sichuan Province,China;Marketing Service Center,State Grid Sichuan Electric Power Company,Chengdu 610042,Sichuan Province,China;State Grid Smart Grid Research Institute Co.,Ltd.,Changping District,Beijing 102211,China)
出处
《电力信息与通信技术》
2023年第6期31-36,共6页
Electric Power Information and Communication Technology
关键词
输电线路
图象识别
缺陷检测
深度学习
power transmission line
image recognition
defect detection
deep learning
作者简介
李旭旭(1981),男,硕士,主要从事电网智能运检、电网数字孪生技术;通信作者:马小敏(1986),男,硕士,主要从事电网智能运检和防灾减灾,lh18830267026@163.com;唐军(1964),男,硕士,主要从事电网数字化技术;孔庆宇(1987),男,硕士,主要从事电力人工智能和电网先进计算;于军亮(1986),男,学士,主要从事电力人工智能和电网先进计算。
