EDF-YOLOv5: An Improved Algorithm for Power Transmission Line Defect Detection Based on YOLOv5

Abstract

Detecting defects in power transmission lines through unmanned aerial inspection images is crucial for evaluating the operational status of outdoor transmission equipment. This paper presents a defect recognition method called EDF-YOLOv5, which is based on the YOLOv5s, to enhance detection accuracy. Firstly, the EN-SPPFCSPC module is designed to improve the algorithm’s ability to extract information, thereby enhancing the detection performance for small target defects. Secondly, the algorithm incorporates a high-level semantic feature information extraction network, DCNv3C3, which improves its ability to generalize to defects of different shapes. Lastly, a new bounding box loss function, Focal-CIoU, is introduced to enhance the contribution of high-quality samples during training. The experimental results demonstrate that the enhanced algorithm achieves a 2.3% increase in mean average precision (mAP@.5) for power transmission line defect detection, a 0.9% improvement in F1-score, and operates at a detection speed of 117 frames per second. These findings highlight the superior performance of EDF-YOLOv5 in detecting power transmission line defects.