Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm
Abstract Thanks to the rapid development of image processing technology, the efficiency and accuracy of power inspection have been enhanced through deep learning techniques. However, during on-site inspections, the complexity of the background images of composite insulators often makes it difficult...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92696-0 |
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| author | Shaotong Pei Weiqi Wang Peng Wu Chenlong Hu Haichao Sun Keyu Li Mianxiao Wu Bo Lan |
| author_facet | Shaotong Pei Weiqi Wang Peng Wu Chenlong Hu Haichao Sun Keyu Li Mianxiao Wu Bo Lan |
| author_sort | Shaotong Pei |
| collection | DOAJ |
| description | Abstract Thanks to the rapid development of image processing technology, the efficiency and accuracy of power inspection have been enhanced through deep learning techniques. However, during on-site inspections, the complexity of the background images of composite insulators often makes it difficult to directly extract key features for accurately assessing hydrophobicity levels. Moreover, considering the real-time requirements for insulator hydrophobicity detection in practical operations, the model must be lightweight to speed up the detection process. To address this issue, this paper proposes a YOLO algorithm for the precise detection of composite insulator hydrophobicity. The algorithm integrates a high-performance GPU network (HGNetv2), a mixed local channel attention mechanism (MLCA), lightweight convolution (CSPPC), and the Inner-WIoU loss function, significantly reducing the network’s burden and improving the accuracy of recognizing composite insulator sheds and classifying their hydrophobicity levels. By adopting a strategy of identifying insulator sheds and then classifying their hydrophobicity levels, precise detection of hydrophobicity is achieved. Experimental results show that the proposed AHC-YOLO algorithm has increased the detection accuracy of sheds by 5.77%, with GFLOPs reduced to 5.8. In the task of classifying hydrophobicity levels, the Top-1 accuracy has been improved by 4.994%, with GFLOPs reduced to 1.9. These achievements not only meet the needs for the detection and classification of composite insulator hydrophobicity but also further demonstrate the effectiveness and superiority of the algorithm through ablation and comparative experiments. |
| format | Article |
| id | doaj-art-e645d1d8e99546049b17c8a5c45cfa06 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e645d1d8e99546049b17c8a5c45cfa062025-08-20T02:41:33ZengNature PortfolioScientific Reports2045-23222025-03-0115111210.1038/s41598-025-92696-0Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithmShaotong Pei0Weiqi Wang1Peng Wu2Chenlong Hu3Haichao Sun4Keyu Li5Mianxiao Wu6Bo Lan7Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power UniversityHebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power UniversityHebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power UniversityHebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power UniversityHebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power UniversityHebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power UniversityHebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power UniversityHebei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power UniversityAbstract Thanks to the rapid development of image processing technology, the efficiency and accuracy of power inspection have been enhanced through deep learning techniques. However, during on-site inspections, the complexity of the background images of composite insulators often makes it difficult to directly extract key features for accurately assessing hydrophobicity levels. Moreover, considering the real-time requirements for insulator hydrophobicity detection in practical operations, the model must be lightweight to speed up the detection process. To address this issue, this paper proposes a YOLO algorithm for the precise detection of composite insulator hydrophobicity. The algorithm integrates a high-performance GPU network (HGNetv2), a mixed local channel attention mechanism (MLCA), lightweight convolution (CSPPC), and the Inner-WIoU loss function, significantly reducing the network’s burden and improving the accuracy of recognizing composite insulator sheds and classifying their hydrophobicity levels. By adopting a strategy of identifying insulator sheds and then classifying their hydrophobicity levels, precise detection of hydrophobicity is achieved. Experimental results show that the proposed AHC-YOLO algorithm has increased the detection accuracy of sheds by 5.77%, with GFLOPs reduced to 5.8. In the task of classifying hydrophobicity levels, the Top-1 accuracy has been improved by 4.994%, with GFLOPs reduced to 1.9. These achievements not only meet the needs for the detection and classification of composite insulator hydrophobicity but also further demonstrate the effectiveness and superiority of the algorithm through ablation and comparative experiments.https://doi.org/10.1038/s41598-025-92696-0Composite insulatorsHydrophobicityDefect detection classificationYOLO |
| spellingShingle | Shaotong Pei Weiqi Wang Peng Wu Chenlong Hu Haichao Sun Keyu Li Mianxiao Wu Bo Lan Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm Scientific Reports Composite insulators Hydrophobicity Defect detection classification YOLO |
| title | Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm |
| title_full | Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm |
| title_fullStr | Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm |
| title_full_unstemmed | Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm |
| title_short | Detection of hydrophobicity grade of insulators based on AHC-YOLO algorithm |
| title_sort | detection of hydrophobicity grade of insulators based on ahc yolo algorithm |
| topic | Composite insulators Hydrophobicity Defect detection classification YOLO |
| url | https://doi.org/10.1038/s41598-025-92696-0 |
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