Insulator Defect Detection Algorithm Based on Improved YOLOv11n
Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. Environmental factors often lead to insulator defects, highlighting the...
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1327 |
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| author | Junmei Zhao Shangxiao Miao Rui Kang Longkun Cao Liping Zhang Yifeng Ren |
| author_facet | Junmei Zhao Shangxiao Miao Rui Kang Longkun Cao Liping Zhang Yifeng Ren |
| author_sort | Junmei Zhao |
| collection | DOAJ |
| description | Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. Environmental factors often lead to insulator defects, highlighting the need for accurate detection methods. This paper proposes an enhanced defect detection approach based on a lightweight neural network derived from the YOLOv11n architecture. Key innovations include a redesigned C3k2 module that incorporates multidimensional dynamic convolutions (ODConv) for improved feature extraction, the introduction of Slimneck to reduce model complexity and computational cost, and the application of the WIoU loss function to optimize anchor box handling and to accelerate convergence. Experimental results demonstrate that the proposed method outperforms existing models like YOLOv8 and YOLOv10 in precision, recall, and mean average precision (mAP), while maintaining low computational complexity. This approach provides a promising solution for real-time, high-accuracy insulator defect detection, enhancing the safety and reliability of power transmission systems. |
| format | Article |
| id | doaj-art-a82a1cbf87f64a16a3b5d47a4a01f613 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a82a1cbf87f64a16a3b5d47a4a01f6132025-08-20T02:52:45ZengMDPI AGSensors1424-82202025-02-01255132710.3390/s25051327Insulator Defect Detection Algorithm Based on Improved YOLOv11nJunmei Zhao0Shangxiao Miao1Rui Kang2Longkun Cao3Liping Zhang4Yifeng Ren5The College of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaThe College of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaThe College of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaThe College of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaThe College of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaThe College of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaEnsuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. Environmental factors often lead to insulator defects, highlighting the need for accurate detection methods. This paper proposes an enhanced defect detection approach based on a lightweight neural network derived from the YOLOv11n architecture. Key innovations include a redesigned C3k2 module that incorporates multidimensional dynamic convolutions (ODConv) for improved feature extraction, the introduction of Slimneck to reduce model complexity and computational cost, and the application of the WIoU loss function to optimize anchor box handling and to accelerate convergence. Experimental results demonstrate that the proposed method outperforms existing models like YOLOv8 and YOLOv10 in precision, recall, and mean average precision (mAP), while maintaining low computational complexity. This approach provides a promising solution for real-time, high-accuracy insulator defect detection, enhancing the safety and reliability of power transmission systems.https://www.mdpi.com/1424-8220/25/5/1327insulator defect detectionyou only look once (YOLO)multidimensional dynamic convolutions (ODConv) |
| spellingShingle | Junmei Zhao Shangxiao Miao Rui Kang Longkun Cao Liping Zhang Yifeng Ren Insulator Defect Detection Algorithm Based on Improved YOLOv11n Sensors insulator defect detection you only look once (YOLO) multidimensional dynamic convolutions (ODConv) |
| title | Insulator Defect Detection Algorithm Based on Improved YOLOv11n |
| title_full | Insulator Defect Detection Algorithm Based on Improved YOLOv11n |
| title_fullStr | Insulator Defect Detection Algorithm Based on Improved YOLOv11n |
| title_full_unstemmed | Insulator Defect Detection Algorithm Based on Improved YOLOv11n |
| title_short | Insulator Defect Detection Algorithm Based on Improved YOLOv11n |
| title_sort | insulator defect detection algorithm based on improved yolov11n |
| topic | insulator defect detection you only look once (YOLO) multidimensional dynamic convolutions (ODConv) |
| url | https://www.mdpi.com/1424-8220/25/5/1327 |
| work_keys_str_mv | AT junmeizhao insulatordefectdetectionalgorithmbasedonimprovedyolov11n AT shangxiaomiao insulatordefectdetectionalgorithmbasedonimprovedyolov11n AT ruikang insulatordefectdetectionalgorithmbasedonimprovedyolov11n AT longkuncao insulatordefectdetectionalgorithmbasedonimprovedyolov11n AT lipingzhang insulatordefectdetectionalgorithmbasedonimprovedyolov11n AT yifengren insulatordefectdetectionalgorithmbasedonimprovedyolov11n |