Research on improved overhead transmission line defect detection algorithm based on YOLOv8

Electric energy is one of the most important fundamental energy sources. As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. To improve the accuracy of recognition, this article proposes a transmission line defect detection algorithm based on improved YOLOv8 to address the practical problems in inspection. Firstly, add full dimensional dynamic convolution to the original algorithm to improve its feature extraction ability and detection accuracy without increasing computational complexity; Then, due to the loss of a large amount of position and feature information after multiple downsampling of the feature map, a dynamic object detection head DyHead (Dynamic Head) combined with attention mechanism is adopted. This detection head integrates scale perception, spatial perception, and task perception to achieve stronger feature expression ability; Finally, use the SIOU loss function to accelerate convergence speed and improve model accuracy. The actual insurance shows that the improved algorithm has a detection accuracy of 84.4%, which is 11.2% higher than the original algorithm. It has good detection accuracy and real-time performance, and effectively solves the problems of missed and false detections.