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23 May 2023 Fine-grained insulator defect detection method based on vision-transformer
Jiani Yang, Libo Yang, Lanlan Liu, Fuli Wan, Wanxia Deng
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Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 1260406 (2023) https://doi.org/10.1117/12.2674512
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China
Abstract
Insulator defects are unavoidable due to long-term exposure and harsh natural environment. With the development of the computer vision and deep learning, researchers have been drawn to automatic unmanned aerial vehicle-based insulator inspection to improve power transmission safety. However, achieving full automation of fine-grained insulator defect detection is still very challenging due to the visual complexity of defects and the high-resolution image computation complexity. This study focuses on fine-grained insulator defect detection by vision transformer based on deep learning. The proposed method is based on a Swin-transformer framework, which focuses on learning hierarchical image feature representation computed with shifted windows scheme. Experiments were carried out on high-resolution image datasets to evaluate the performance of the proposed method for fine-grained insulator defect detection tasks. The results show that the method takes advantage of vision transformer's capabilities and outperforms the state-of-the-art method in terms of mean average precision (mAP) at 94.2% when the intersection threshold over union is set to 0.5.
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Jiani Yang, Libo Yang, Lanlan Liu, Fuli Wan, and Wanxia Deng "Fine-grained insulator defect detection method based on vision-transformer", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 1260406 (23 May 2023); https://doi.org/10.1117/12.2674512
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KEYWORDS
Object detection
Defect detection
Transformers
Inspection
Deep learning
Power grids
Unmanned aerial vehicles
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