A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines
Abstract
:1. Introduction
- A small-scale transmission-line defect dataset is constructed to address the lack of publicly available datasets. This self-built dataset includes a variety of typical defects in transmission lines, which are annotated to meet the requirements for model training and validation;
- A two-stage transmission-line defect detection framework based on YOLOv5s and U-Net is proposed. By combining the advantages of detection and segmentation, this framework achieves the collaborative processing of global and local image features. For strand breakage, characterized by high contrast and structural mutations, YOLOv5s is used for direct localization. For surface-damage areas with fuzzy edges and low contrast, U-Net is used for fine pixel-level segmentation after initial detection, enhancing the recognition accuracy of complex defects;
- Transfer learning and loss function optimization are introduced to improve model performance. YOLOv5s is initialized with COCO pretrained weights, and U-Net uses a VGG16 encoder pretrained on ImageNet as the feature-extraction module. A composite loss function combining Dice Loss and Focal Loss is constructed to alleviate issues related to class imbalance and small-target recognition;
- By comparing it with single detection or segmentation models, the proposed two-stage method demonstrates advantages in detection efficiency, segmentation details, and robustness, achieving improved defect-recognition precision and detail retention.
2. Methods
2.1. Model Structure of This Paper
Overall Framework Design
- Global defect localization: The original inspection image is fed into the YOLOv5s network, which outputs bounding boxes and confidence scores for potential defect areas, and directly locates strand-breakage regions;
- Candidate region cropping: A relaxed confidence threshold (>0.25) is applied to maximize the capture of surface damage, including those with weak features. Lowering the threshold improves recall, ensuring that small or low-contrast defects proceed to the segmentation stage;
- Local fine segmentation: Cropped ROIs (Regions of Interest) are resized to a standard input size and passed into the pretrained U-Net model, which outputs pixel-level masks representing the exact shapes and edges of the defects.
2.2. Two-Stage Model Framework
2.2.1. Global Detection
- Input layer
- 2.
- Backbone
- 3.
- Neck
- 4.
- Head
2.2.2. Local Fine Segmentation
- Transfer learning
- 2.
- Loss function
3. Results and Discussion
3.1. Datasets
3.2. Experimental Environment
3.3. Evaluation Indicators
- Recall
- 2.
- Precision
- 3.
- mAP
- Dice Coefficient;
- 2.
- MIoU
- 3.
- Precision
3.4. Analysis of Experimental Results
3.4.1. Target-Detection Performance Analysis
3.4.2. Semantic Segmentation Performance Analysis
3.4.3. Model Advantages
- Precision reflects the proportion of correctly identified defects among all detected instances. A higher precision indicates fewer false positives, which is critical for ensuring the safety of transmission systems;
- Recall measures the model’s ability to identify actual defects. A higher recall implies fewer missed detections, which is especially important for high-risk scenarios such as transmission-line monitoring to ensure that no defects are overlooked;
- Inference time directly affects the deployment efficiency and real-time applicability of the model. It is an essential metric to assess the system’s responsiveness and engineering viability in practical inspection tasks.
3.4.4. Future Improvement Directions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model | Precision | Recall | Inference Time (s) |
---|---|---|---|
YOLOv5s (Single) | 0.89 | 0.85 | 0.05 |
U-Net (Single) | 0.87 | 0.87 | 0.12 |
YOLOv5s + U-Net (Two-stage) | 0.91 | 0.89 | 0.08 |
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Li, A.; Li, D.; Wang, A. A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines. Sensors 2025, 25, 2903. https://doi.org/10.3390/s25092903
Li A, Li D, Wang A. A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines. Sensors. 2025; 25(9):2903. https://doi.org/10.3390/s25092903
Chicago/Turabian StyleLi, Aohua, Dacheng Li, and Anjing Wang. 2025. "A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines" Sensors 25, no. 9: 2903. https://doi.org/10.3390/s25092903
APA StyleLi, A., Li, D., & Wang, A. (2025). A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines. Sensors, 25(9), 2903. https://doi.org/10.3390/s25092903