Automatic Sensing and Detection for Subway Tunnel Pathologies
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Visual detection
Neural network
Health monitoring
Image segmentation
Water leakage
Subway tunnel



Submitted : 2024-01-27
Accepted : 2024-02-11
Published : 2024-02-26


Subway tunnels often suffer from surface pathologies such as cracks, corrosion, fractures, peeling, water and sand infiltration, and sudden hazards caused by foreign object intrusions. Installing a mobile visual pathology sensing system at the front end of operating trains is a critical measure to ensure subway safety. Taking leakage as the typical pathology, a tunnel pathology automatic visual detection method based on Deeplabv3+ (ASTPDS) was proposed to achieve automatic and high-precision detection and pixel-level morphology extraction of pathologies. Compared with similar methods, this approach showed significant advantages and achieved a detection accuracy of 93.12%, surpassing FCN and U-Net. Moreover, it also exceeded the recall rates for detecting leaks of FCN and U-Net by 8.33% and 8.19%, respectively.


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