YOLOv7-CWFD for real time detection of bolt defects on transmission lines
Abstract Detecting bolt defects on transmission lines is crucial for ensuring the safe operation of the electrical power system. However, existing methods for detecting bolt defects on transmission lines require higher detection accuracy and smaller model sizes. To address these challenges, this pap...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-81386-y |
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| author | Lincong Peng Kerui Wang Hao Zhou Yi Ma Pengfei Yu |
| author_facet | Lincong Peng Kerui Wang Hao Zhou Yi Ma Pengfei Yu |
| author_sort | Lincong Peng |
| collection | DOAJ |
| description | Abstract Detecting bolt defects on transmission lines is crucial for ensuring the safe operation of the electrical power system. However, existing methods for detecting bolt defects on transmission lines require higher detection accuracy and smaller model sizes. To address these challenges, this paper proposes a real-time bolt defect detection model based on YOLOv7, named YOLOv7-CWFD. The model integrates the Channel Shuffle Diverse Path Aggregation Network (CSDPAN), significantly reducing computational and parameter complexity while maintaining high detection accuracy. Additionally, weighted Efficient Intersection over Union (EIoU) and Normalized Wasserstein Distance (NWD) loss functions are designed to reduce the network’s sensitivity to object size variations and enhance model convergence in regression tasks. The Fast Fourier Channel Attention Mechanism (FFCAM) is introduced between the backbone and neck fusion networks to mitigate excessive smoothing of detailed information and improve the network’s sensitivity to objects. The DySample upsampling operator is implemented to replace the upsampling module in the neck fusion network, minimizing information loss during the upsampling process. Experiments conducted on the custom Transmission Line Bolt Defect Dataset (TLBDD) demonstrate a reduction of 10.30MB in model parameter size, along with a 2.30% increase in mean Average Precision (mAP) compared with the original YOLOV7 and a detection speed of 51.15 frames per second (FPS). Experiments on the public dataset CCTSDB further confirm the model’s robust generalization capability. These experiments validate the effectiveness of the proposed algorithm. |
| format | Article |
| id | doaj-art-e9a3d94ccc454b5084b05c53bb7d7baa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e9a3d94ccc454b5084b05c53bb7d7baa2025-01-12T12:16:47ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-024-81386-yYOLOv7-CWFD for real time detection of bolt defects on transmission linesLincong Peng0Kerui Wang1Hao Zhou2Yi Ma3Pengfei Yu4School of Information, Yunnan UniversitySchool of Information, Yunnan UniversitySchool of Information, Yunnan UniversityYunnan Power Grid CorporationSchool of Information, Yunnan UniversityAbstract Detecting bolt defects on transmission lines is crucial for ensuring the safe operation of the electrical power system. However, existing methods for detecting bolt defects on transmission lines require higher detection accuracy and smaller model sizes. To address these challenges, this paper proposes a real-time bolt defect detection model based on YOLOv7, named YOLOv7-CWFD. The model integrates the Channel Shuffle Diverse Path Aggregation Network (CSDPAN), significantly reducing computational and parameter complexity while maintaining high detection accuracy. Additionally, weighted Efficient Intersection over Union (EIoU) and Normalized Wasserstein Distance (NWD) loss functions are designed to reduce the network’s sensitivity to object size variations and enhance model convergence in regression tasks. The Fast Fourier Channel Attention Mechanism (FFCAM) is introduced between the backbone and neck fusion networks to mitigate excessive smoothing of detailed information and improve the network’s sensitivity to objects. The DySample upsampling operator is implemented to replace the upsampling module in the neck fusion network, minimizing information loss during the upsampling process. Experiments conducted on the custom Transmission Line Bolt Defect Dataset (TLBDD) demonstrate a reduction of 10.30MB in model parameter size, along with a 2.30% increase in mean Average Precision (mAP) compared with the original YOLOV7 and a detection speed of 51.15 frames per second (FPS). Experiments on the public dataset CCTSDB further confirm the model’s robust generalization capability. These experiments validate the effectiveness of the proposed algorithm.https://doi.org/10.1038/s41598-024-81386-y |
| spellingShingle | Lincong Peng Kerui Wang Hao Zhou Yi Ma Pengfei Yu YOLOv7-CWFD for real time detection of bolt defects on transmission lines Scientific Reports |
| title | YOLOv7-CWFD for real time detection of bolt defects on transmission lines |
| title_full | YOLOv7-CWFD for real time detection of bolt defects on transmission lines |
| title_fullStr | YOLOv7-CWFD for real time detection of bolt defects on transmission lines |
| title_full_unstemmed | YOLOv7-CWFD for real time detection of bolt defects on transmission lines |
| title_short | YOLOv7-CWFD for real time detection of bolt defects on transmission lines |
| title_sort | yolov7 cwfd for real time detection of bolt defects on transmission lines |
| url | https://doi.org/10.1038/s41598-024-81386-y |
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