A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines
Transmission-line defect detection is crucial for grid operation. Existing methods struggle to balance defect localization and fine segmentation. Therefore, this study proposes a novel cascaded two-stage framework that first utilizes YOLOv5s for the global localization of defective regions, and then...
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MDPI AG
2025-05-01
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| author | Aohua Li Dacheng Li Anjing Wang |
| author_facet | Aohua Li Dacheng Li Anjing Wang |
| author_sort | Aohua Li |
| collection | DOAJ |
| description | Transmission-line defect detection is crucial for grid operation. Existing methods struggle to balance defect localization and fine segmentation. Therefore, this study proposes a novel cascaded two-stage framework that first utilizes YOLOv5s for the global localization of defective regions, and then uses U-Net for the fine segmentation of candidate regions. To improve the segmentation performance, U-Net adopts a transfer learning strategy based on the VGG16 pretrained model to alleviate the impact of limited dataset size on the training effect. Meanwhile, a hybrid loss function that combines Dice Loss and Focal Loss is designed to solve the small-target and class imbalance problems. This method integrates target detection and fine segmentation, enhancing detection precision and improving the extraction of detailed damage features. Experiments on the self-constructed dataset show that the method achieves 87% mAP on YOLOv5s, 88% U-Net damage recognition precision, a mean Dice coefficient of 93.66%, and 89% mIoU, demonstrating its effectiveness in accurately detecting transmission-line defects and efficiently segmenting the damage region, providing assistance for the intelligent operation and maintenance of transmission lines. |
| format | Article |
| id | doaj-art-125cedb256784ffbbceefcd4afd98a08 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-125cedb256784ffbbceefcd4afd98a082025-08-20T02:58:48ZengMDPI AGSensors1424-82202025-05-01259290310.3390/s25092903A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission LinesAohua Li0Dacheng Li1Anjing Wang2School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, ChinaAnhui Institute of Optics and Fine Mechanics, Key Laboratory of General Optical Calibration and Characterization Technology, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Institute of Optics and Fine Mechanics, Key Laboratory of General Optical Calibration and Characterization Technology, Chinese Academy of Sciences, Hefei 230031, ChinaTransmission-line defect detection is crucial for grid operation. Existing methods struggle to balance defect localization and fine segmentation. Therefore, this study proposes a novel cascaded two-stage framework that first utilizes YOLOv5s for the global localization of defective regions, and then uses U-Net for the fine segmentation of candidate regions. To improve the segmentation performance, U-Net adopts a transfer learning strategy based on the VGG16 pretrained model to alleviate the impact of limited dataset size on the training effect. Meanwhile, a hybrid loss function that combines Dice Loss and Focal Loss is designed to solve the small-target and class imbalance problems. This method integrates target detection and fine segmentation, enhancing detection precision and improving the extraction of detailed damage features. Experiments on the self-constructed dataset show that the method achieves 87% mAP on YOLOv5s, 88% U-Net damage recognition precision, a mean Dice coefficient of 93.66%, and 89% mIoU, demonstrating its effectiveness in accurately detecting transmission-line defects and efficiently segmenting the damage region, providing assistance for the intelligent operation and maintenance of transmission lines.https://www.mdpi.com/1424-8220/25/9/2903transmission-line defectsdefect localizationsemantic segmentationYOLOv5sU-nettwo-stage framework |
| spellingShingle | Aohua Li Dacheng Li Anjing Wang A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines Sensors transmission-line defects defect localization semantic segmentation YOLOv5s U-net two-stage framework |
| title | A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines |
| title_full | A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines |
| title_fullStr | A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines |
| title_full_unstemmed | A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines |
| title_short | A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines |
| title_sort | two stage yolov5s u net framework for defect localization and segmentation in overhead transmission lines |
| topic | transmission-line defects defect localization semantic segmentation YOLOv5s U-net two-stage framework |
| url | https://www.mdpi.com/1424-8220/25/9/2903 |
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