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Research on intelligent recognition algorithm for subsurface road defects
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PRE
AI
DOI：10.1080/10589759.2025.2577833.png)
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Abstract
En 中文
In recent years, frequent urban road collapses have led to casualties and severely compromised traffic safety. Currently, 3D Ground Penetrating Radar (GPR) technology is recognised as the most scientific and effective method for rapidly screening defects to prevent and mitigate the risk of road collapses. However, interpreting radar images still faces challenges such as heavy reliance on manual expertise, large data processing requirements, and low efficiency. Therefore, exploring AI-driven methods for the intelligent detection of underground road defects is of significant importance. This study enhances the original YOLOv5 algorithm by introducing a dedicated small-target detection layer and replacing the C3 backbone with the lightweight GhostNet architecture using Python platform, aiming to improve the precision and efficiency of detecting small-scale defects. Experimental results demonstrate that the optimised YOLOv5 achieves notable improvements in small-target detection, including increased precision, recall, accuracy, F1-score, and faster processing speed. In practical engineering applications, the proposed method achieves full detection of defect location with no missed cases. The rate of multiple identifications is approximately 22%, and the detection time is about 46% of the average time required for manual interpretation, meeting the requirements of real-world engineering applications.
Keywords:
Road collapse
radar images
YOLOv5 algorithm
defect recognition
Journal
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4.2
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1.7K
Citations:
2.1K