Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7

Abstract

Efficient and accurate insulator defect detection is essential for maintaining the safe and stable operation of transmission lines. However, the detection effectiveness is adversely impacted by complex and changeable environmental backgrounds, particularly under extreme weather that elevates accident risks. Therefore, this research proposes a high-precision intelligent strategy based on the synthetic weather algorithm and improved YOLOv7 for detecting insulator defects under extreme weather. The proposed methodology involves augmenting the dataset with synthetic rain, snow, and fog algorithm processing. Additionally, the original dataset undergoes augmentation through affine and colour transformations to improve model's generalisation performance under complex power inspection backgrounds. To achieve higher recognition accuracy in severe weather, an improved YOLOv7 algorithm for insulator defect detection is proposed, integrating focal loss with SIoU loss function and incorporating an optimised decoupled head structure. Experimental results indicate that the synthetic weather algorithm processing significantly improves the insulator defect detection accuracy under extreme weather, increasing the mean average precision by 2.4%. Furthermore, the authors’ improved YOLOv7 model achieves 91.8% for the mean average precision, outperforming the benchmark model by 2.3%. With a detection speed of 46.5 frames per second, the model meets the requirement of real-time detection of insulators and their defects during power inspection.