Transmission Line Component Recognition Method Based on UAV Automatic Inspection Technology

Transmission line equipment operates in complex and variable outdoor environments over long periods, which leads to various defects and hidden risks that can compromise the safe operation of power lines. To ensure the safety and reliability of transmission lines, UAV-based inspections have become an effective solution. While UAVs enhance the efficiency and quality of overhead transmission line inspections, the vast amount of image data generated can result in false positives and missed defect detections. To address these challenges, this paper presents a YOLOv6-based algorithm for identifying transmission line components and detecting defects. In addition, the proposed algorithm has been optimized for lightweight deployment, allowing it to be integrated directly into UAVs. This integration enables real-time defect detection during UAV inspections, enhancing the overall efficiency and accuracy of the process.