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Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
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CLGDS: robust bridge crack detection with YOLO enhanced feature fusion and SIoU optimization
Bao Jiao1, Xiao Canjun2, Guo Dong3
1Institute of Mine Intelligence, Chengdu Technological University, Chengdu, 610031, China.
Scientific Reports
|April 4, 2026
Summary
This study introduces CLGDS, a novel method for robust bridge crack detection. CLGDS significantly enhances accuracy and robustness in identifying cracks, crucial for structural health monitoring and maintenance.
Area of Science:
- Structural Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Crack detection is vital for bridge structural integrity and maintenance.
- Existing methods face challenges with low-contrast backgrounds and diverse crack features.
Purpose of the Study:
- To develop a robust crack detection method for bridges.
- To improve accuracy and robustness in identifying cracks of various scales and shapes.
Main Methods:
- Proposed CLGDS method based on YOLO11, featuring a Cross Stage Partially Large Separable Kernel Attention (C2LSKA) module.
- Integrated a Gathering and Distributing (GD) mechanism for multi-scale feature fusion.
- Utilized Scylla-IoU (SIoU) loss function for improved bounding box regression.
Main Results:
- CLGDS achieved a mean average precision (mAP@50) of 93.5%, outperforming YOLOv5, YOLOv8, and YOLO11.
- Attained a mAP@50-95 of 68.5%, significantly higher than baseline models.
- Demonstrated superior performance in detecting cracks with varying scales and complex backgrounds.
Conclusions:
- CLGDS offers effective and robust bridge crack detection.
- Provides a strong technical foundation for automated structural health monitoring.
- Enhances preventive maintenance strategies for bridges.
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