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 studies 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. Furthermore

the effectiveness of the proposed method is verified using an augmented obstacle data set 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.