Small Target Bird Nest Detection Method for Power Transmission Lines Based on Improved YOLOv5 Algorithm with Wasserstein Distance Loss

The presence of bird nests on transmission and transformation lines poses a serious threat to the safe operation of the power system. Short circuits and trips caused by bird nests can lead to widespread power outages. This paper proposes an improved algorithm that combines YOLOV5 with attention mechanism and adds detection heads for small targets, for detecting bird nests on power transmission lines. By introducing attention mechanism, this algorithm can dynamically focus on important features in the bird’s nest area, enhancing its ability to extract key regions in complex background images. While ensuring high detection accuracy, the attention mechanism further enhances the robustness and feature expression ability of the model in complex scenes. The experimental results show that the YOLOV5 algorithm, which utilizes the Wasserstein distance loss function, combines attention mechanism, and adds a small object detection head, not only significantly improves accuracy, but also outperforms traditional methods in real-time and detection efficiency, providing more effective technical support for automated inspection of transmission and transformation lines.