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

Ming Zhou; Qiang Wu; Wei Xiong; Li Ma; Zhao Cheng; Liping Sun

doi:10.1117/12.3049575

8 November 2024 Enhanced YOLOv8 for rapid diagnosis and analysis of infrared spectra in power industry insulator detection

Ming Zhou, Qiang Wu, Wei Xiong, Li Ma, Zhao Cheng, Liping Sun

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134163F (2024) https://doi.org/10.1117/12.3049575
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming Zhou, Qiang Wu, Wei Xiong, Li Ma, Zhao Cheng, and Liping Sun "Enhanced YOLOv8 for rapid diagnosis and analysis of infrared spectra in power industry insulator detection", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134163F (8 November 2024); https://doi.org/10.1117/12.3049575

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