Research on the algorithm of detecting insulators in high-voltage transmission lines using UAV images

Insulator defect detection is an important task of power line inspection, but because the insulator defect target occupies a small area in the transmission line image, and the background is complex and changeable, the accuracy of insulator defect detection is low. Based on previous research results and existing problems, an insulator defect detection algorithm based on YOLOv5 is proposed. Based on the original YOLOv5, C2f module is proposed to replace C3 module in backbone part of YOLOv5, Wise-IoU loss function is introduced, and SimAM attention mechanism is added to head part of the original YOLOv5. As for the above improvement, the method can accurately and effectively identify the insulator and accurately judge whether there is a defect problem under the complex background. We conducted training and comparison experiments on different models on the CPLID insulator public data set and the custom data set of Roboflow and designed ablation experiments to analyze the effects of each improvement part. The experimental results show that the detection accuracy of the improved YOLOv5n model in the insulator defect target detection task reaches \(97.7 \%\) $97.7\%$ and mAP50-95 reaches 78.7%. Compared with the original YOLOv5n model, the accuracy of the improved YOLOv5 model is increased by \(0.6 \%\) $0.6\%$ and mAP50-95 by 1.1 percentage points. The improved algorithm can be better applied to the target detection of insulator defects in UAV. Considering the high probability that mobile devices do not support high-performance Gpus and the simple network structure of YOLOv5 is more lightweight than the YOLOv8 model, we chose the 5 version of YOLO instead of the latest YOLOv8.