电测与仪表2026，Vol.63Issue(1)：160-167,8.DOI:10.19753/j.issn1001-1390.2026.01.017
基于深度学习的架空输电线附属障碍物识别研究
Research on deep learning-based affiliated obstacle identification of overhead transmission line
摘要
Abstract
The overhead line patrol robot plays an important role in improving the line maintenance efficiency and ensuring the safe and stable operation of power system.Aiming at the problem that the overhead line patrol robot needs to effectively identify affiliated obstacles and adopt corresponding obstacle-avoidance actions,this paper stud-ies the deep learning-based affiliated obstacle identification method of overhead transmission line for patrol robots.The overall structure of overhead line patrol robot based on deep learning is discussed.The YOLOv8(you only look once version 8)model and its application in obstacle identification are analyzed based on the overall structure.Fur-thermore,the effectiveness of the proposed method is verified using an augmented obstacle dataset of overhead line.Experimental results show that the proposed overhead line obstacle recognition method has a faster recognition speed and a higher recognition rate,which can meet the obstacle avoidance needs of patrol robots.关键词
架空线/障碍规避/YOLOv8/深度学习Key words
overhead line/obstacle avoidance/YOLOv8/deep learning分类
信息技术与安全科学引用本文复制引用
迟兴江,潘金虎,耿军伟..基于深度学习的架空输电线附属障碍物识别研究[J].电测与仪表,2026,63(1):160-167,8.基金项目
国家电网公司科技项目(3456SKEX203325SO) （3456SKEX203325SO）
