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Method for detecting cracks in retaining walls based on improved YOLOv5s.
Yanhai Wang1,2, Chenxin Guo3, Guoyong Duan1,2
1Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University), Ministry of Education, Yichang, 443002, China.
Scientific Reports
|February 20, 2025
Summary
This study introduces an enhanced YOLOv5s model for detecting small, irregular retaining wall cracks. The improved algorithm offers better crack localization and quantity calculation with fewer parameters, making it ideal for edge-based structural health monitoring.
Area of Science:
- Civil Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Retaining wall cracks pose significant structural risks.
- Detecting small, irregularly shaped cracks is challenging for traditional methods.
Purpose of the Study:
- To develop an improved YOLOv5s-based method for accurate retaining wall crack detection.
- To enhance crack localization, quantity calculation, and model efficiency.
Main Methods:
- Incorporated BotNet and GhostNetV2 modules into the YOLOv5s Backbone for feature extraction and reduced complexity.
- Utilized ODConv module in the Neck for multi-scale feature enhancement.
- Implemented rotated bounding boxes for precise localization of irregular cracks.
Main Results:
- Reduced YOLOv5s parameters by 12% while improving average precision by 1.5% compared to baseline YOLOv5s with rotated frames.
- Demonstrated superior performance in crack localization and quantity calculation.
- Achieved a smaller parameter volume, suitable for edge-based detection.
Conclusions:
- The proposed enhanced YOLOv5s algorithm is an effective tool for retaining wall crack detection.
- The method offers improved accuracy, efficiency, and localization capabilities.
- This approach advances structural health monitoring for critical infrastructure.
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