Simplify-YOLOv5m: A Simplified High-Speed Insulator Detection Algorithm for UAV Images

As an essential power device, insulators are present in large numbers across various transmission lines. Insulators are continuously affected by the natural environment, leading to fractures, pollution accumulation, and other issues. These problems can cause flashovers and pollution flash discharge accidents, trigger relay protection devices, and result in significant economic losses. It is, however, challenging for existing algorithms to achieve a good balance between accuracy and speed in many insulator datasets. In this article, therefore, we construct a novel feature extraction module named the point-depth (PD module) to reduce feature loss and computational complexity. We also designed the ShortPath-FPN (SP-FPN), which introduces the attentional feature fusion (AFF) module to effectively shorten the information flow from input to head and enhance feature maps. Compared to state-of-the-art (SOTA) algorithms, the PD module significantly reduces the amount of computation with only a slight impact on accuracy, while the SP-FPN module effectively improves both accuracy and detection speed. We enhance the YOLOV5m model based on the above modules to achieve an accuracy of 94.1% and an FPS of 77 for insulator detection, meeting the requirements for real-time detection while improving detection accuracy and outperforming other methods.