Insulator Defect Detection in Power Transmission and Transformation Based on the Diffusion Model

Insulators play a crucial role in both power trans-mission and transformation scenarios, and leveraging object detection to analyze insulator defects in images holds significant value for power production. This paper focuses on the distinct aspects of candidate box processing in object detection models, utilizing the diffusion model for perceptual understanding of images to enhance the model's performance in insulator defect detection. The proposed model demonstrates a performance improvement of +6% and 2.9% over Faster R-CNN and DETR, respectively, in power transmission insulator defect detection. Similarly, in power transformation insulator defect detection, the model achieves improvements of +5.8% and +1.7% over Faster R-CNN and DETR, respectively, thereby achieving state-of-the-art results.