Li Aohua, Li Dacheng, Wang Anjing
School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China.
Anhui Institute of Optics and Fine Mechanics, Key Laboratory of General Optical Calibration and Characterization Technology, Chinese Academy of Sciences, Hefei 230031, China.
Sensors (Basel). 2025 May 4;25(9):2903. doi: 10.3390/s25092903.
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.
输电线路缺陷检测对电网运行至关重要。现有方法难以在缺陷定位和精细分割之间取得平衡。因此，本研究提出了一种新颖的级联两阶段框架，该框架首先利用YOLOv5s对缺陷区域进行全局定位，然后使用U-Net对候选区域进行精细分割。为了提高分割性能，U-Net采用基于VGG16预训练模型的迁移学习策略，以减轻数据集规模有限对训练效果的影响。同时，设计了一种结合Dice Loss和Focal Loss的混合损失函数，以解决小目标和类别不平衡问题。该方法将目标检测与精细分割相结合，提高了检测精度，改善了详细损伤特征的提取。在自建数据集上的实验表明，该方法在YOLOv5s上实现了87%的平均精度均值（mAP）、U-Net损伤识别精度为88%、平均Dice系数为93.66%、交并比（mIoU）为89%，证明了其在准确检测输电线路缺陷和有效分割损伤区域方面的有效性，为输电线路的智能运维提供了帮助。
