Enhanced YOLOv8 for rapid diagnosis and analysis of infrared spectra in power industry insulator detection

Insulator defects in power transmission systems pose significant risks to grid stability and safety. This paper presents an improved method for rapid diagnosis of insulator defects using infrared images, based on an enhanced YOLOv8algorithm. Our approach incorporates Haar wavelet downsampling and a Polarized Self-Attention (PSA) mechanism to boost detection accuracy while maintaining real-time performance. The Haar wavelet downsampling Boosts the model's capability to grasp multi-scale features, concurrently enhancing the PSA mechanism's emphasis on pertinent thermal patterns. We evaluate our method on a comprehensive dataset of 10,000 infrared images collected from various power transmission environments. Experimental results demonstrate that our improved YOLOv8 model achieves SOTA, with a mean Average Precision of 94.7%, outperforming the original YOLOv8 and other object detection algorithms. The proposed method offers a 3.2% increase in mAP compared to the original YOLOv8, while maintaining a processing speed of 42.8 frames per second. This research contributes significantly to the advancement of more efficient and precise outcomes power grid maintenance techniques, potentially reducing the need for manual inspections and enhancing overall system reliability.