Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7
Abstract Efficient and accurate insulator defect detection is essential for maintaining the safe and stable operation of transmission lines. However, the detection effectiveness is adversely impacted by complex and changeable environmental backgrounds, particularly under extreme weather that elevate...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | High Voltage |
| Online Access: | https://doi.org/10.1049/hve2.12513 |
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| _version_ | 1850027497679224832 |
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| author | Yong Yang Shuai Yang Chuan Li Yunxuan Wang Xiaoqian Pi Yuxin Lu Ruohan Wu |
| author_facet | Yong Yang Shuai Yang Chuan Li Yunxuan Wang Xiaoqian Pi Yuxin Lu Ruohan Wu |
| author_sort | Yong Yang |
| collection | DOAJ |
| description | Abstract Efficient and accurate insulator defect detection is essential for maintaining the safe and stable operation of transmission lines. However, the detection effectiveness is adversely impacted by complex and changeable environmental backgrounds, particularly under extreme weather that elevates accident risks. Therefore, this research proposes a high‐precision intelligent strategy based on the synthetic weather algorithm and improved YOLOv7 for detecting insulator defects under extreme weather. The proposed methodology involves augmenting the dataset with synthetic rain, snow, and fog algorithm processing. Additionally, the original dataset undergoes augmentation through affine and colour transformations to improve model's generalisation performance under complex power inspection backgrounds. To achieve higher recognition accuracy in severe weather, an improved YOLOv7 algorithm for insulator defect detection is proposed, integrating focal loss with SIoU loss function and incorporating an optimised decoupled head structure. Experimental results indicate that the synthetic weather algorithm processing significantly improves the insulator defect detection accuracy under extreme weather, increasing the mean average precision by 2.4%. Furthermore, the authors’ improved YOLOv7 model achieves 91.8% for the mean average precision, outperforming the benchmark model by 2.3%. With a detection speed of 46.5 frames per second, the model meets the requirement of real‐time detection of insulators and their defects during power inspection. |
| format | Article |
| id | doaj-art-6d0fda8c99f34929b2cb23c3c2bd8fcd |
| institution | DOAJ |
| issn | 2397-7264 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | High Voltage |
| spelling | doaj-art-6d0fda8c99f34929b2cb23c3c2bd8fcd2025-08-20T03:00:09ZengWileyHigh Voltage2397-72642025-02-01101697710.1049/hve2.12513Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7Yong Yang0Shuai Yang1Chuan Li2Yunxuan Wang3Xiaoqian Pi4Yuxin Lu5Ruohan Wu6State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan ChinaState Grid Hunan Electric Power Company Changsha ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan ChinaAbstract Efficient and accurate insulator defect detection is essential for maintaining the safe and stable operation of transmission lines. However, the detection effectiveness is adversely impacted by complex and changeable environmental backgrounds, particularly under extreme weather that elevates accident risks. Therefore, this research proposes a high‐precision intelligent strategy based on the synthetic weather algorithm and improved YOLOv7 for detecting insulator defects under extreme weather. The proposed methodology involves augmenting the dataset with synthetic rain, snow, and fog algorithm processing. Additionally, the original dataset undergoes augmentation through affine and colour transformations to improve model's generalisation performance under complex power inspection backgrounds. To achieve higher recognition accuracy in severe weather, an improved YOLOv7 algorithm for insulator defect detection is proposed, integrating focal loss with SIoU loss function and incorporating an optimised decoupled head structure. Experimental results indicate that the synthetic weather algorithm processing significantly improves the insulator defect detection accuracy under extreme weather, increasing the mean average precision by 2.4%. Furthermore, the authors’ improved YOLOv7 model achieves 91.8% for the mean average precision, outperforming the benchmark model by 2.3%. With a detection speed of 46.5 frames per second, the model meets the requirement of real‐time detection of insulators and their defects during power inspection.https://doi.org/10.1049/hve2.12513 |
| spellingShingle | Yong Yang Shuai Yang Chuan Li Yunxuan Wang Xiaoqian Pi Yuxin Lu Ruohan Wu Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7 High Voltage |
| title | Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7 |
| title_full | Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7 |
| title_fullStr | Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7 |
| title_full_unstemmed | Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7 |
| title_short | Insulator defect detection under extreme weather based on synthetic weather algorithm and improved YOLOv7 |
| title_sort | insulator defect detection under extreme weather based on synthetic weather algorithm and improved yolov7 |
| url | https://doi.org/10.1049/hve2.12513 |
| work_keys_str_mv | AT yongyang insulatordefectdetectionunderextremeweatherbasedonsyntheticweatheralgorithmandimprovedyolov7 AT shuaiyang insulatordefectdetectionunderextremeweatherbasedonsyntheticweatheralgorithmandimprovedyolov7 AT chuanli insulatordefectdetectionunderextremeweatherbasedonsyntheticweatheralgorithmandimprovedyolov7 AT yunxuanwang insulatordefectdetectionunderextremeweatherbasedonsyntheticweatheralgorithmandimprovedyolov7 AT xiaoqianpi insulatordefectdetectionunderextremeweatherbasedonsyntheticweatheralgorithmandimprovedyolov7 AT yuxinlu insulatordefectdetectionunderextremeweatherbasedonsyntheticweatheralgorithmandimprovedyolov7 AT ruohanwu insulatordefectdetectionunderextremeweatherbasedonsyntheticweatheralgorithmandimprovedyolov7 |