Abstract: A fault identification method for transmission lines based on improved YOLO v8 was proposed to address the challenges of difficult inspection of transmission lines and poor reliability of inspection information processing.Firstly,the intelligent individual inspection equipment was designed,including intelligent inspection helmets and intelligent information equipment suits,and operate drones to obtain real-time operation status of transmission lines.Then,an incremental octree spatial retrieval algorithm was proposed to process the image information such as LiDAR to obtain panoramic images of transmission lines.Finally,an improved C2f module,residual attention module,and improved loss function were constructed to optimize the YOLO v8 model,which was used for panoramic image learning to obtain the fault types of transmission lines.Based on the Pytorch platform,experimental analysis was conducted on the proposed method,and the results showed that the mPA of the recognition result reached 92.03%,and the recognition time was only 28 ms,which can meet the work requirements of intelligent individual patrol equipment.