[1]ZHAO Zhenbing,FENG Shuo,ZHAO Wenqing,et al.Thermd image detection method for substation equipment by incorporating knowledge migration and improved YOLOv6[J].CAAI Transactions on Intelligent Systems,2023,18(6):1213-1222.[doi:10.11992/tis.202303030]
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Thermd image detection method for substation equipment by incorporating knowledge migration and improved YOLOv6

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1213-1222 Column: 学术论文—机器感知与模式识别 Public date: 2023-11-05
Title:
Thermd image detection method for substation equipment by incorporating knowledge migration and improved YOLOv6
Author(s):
ZHAO Zhenbing1; 2; 3;  FENG Shuo1;  ZHAO Wenqing2; 4;  ZHAI Yongjie4;  WANG Hongtao2; 4
1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;
2. Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, North China Electric Power University, Baoding 071003, China;
3. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China;
4. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
substation equipment; thermal infrared image; knowledge migration; sample generation; object detection; diffusion model; data augmentation; deep learning
CLC:
TP18
DOI:
10.11992/tis.202303030
Abstract:
To address the problems of insufficient complex background samples and difficulty in device location in substation equipment thermal image detection, a fusion knowledge transfer and improved YOLOv6 detection method are proposed. The diffusion model was used to extract background knowledge from extraterritorial data for generating background images, solving the problem of insufficient complex background samples. The device samples were then migrated to the background images to generate artificial images. The multi-head self-attention mechanism and explicit visual center module were integrated into YOLOv6 to improve its feature extraction capability, solving the issue of difficulty in detecting devices. The experiment shows that the mAP and mAR of the proposed method reach 86.4% and 89.4%, indicating an improvement of 3.1% and 1.5% compared to the baseline model, respectively. This study provides a new implementation method for thermal image detection of substation equipment.
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