Advancing Tunnel Crack Detection with AI for Safer Infrastructure Inspection and Monitoring

Automatic detection of tunnel lining cracks has been widely studied for infrastructure safety, but reliable recognition in real operating tunnels remains challenging. Visual interference from glare, shadows, seepage traces, segment joints, and textured backgrounds can obscure fine or discontinuous cracks, making missed detection a major practical risk.

In a study published in the Journal of Railway Science and Technology, a team of researchers proposed an enhanced U-Net architecture for tunnel lining crack segmentation known as CSwinUKAN.

“The model combines a pretrained Swin Transformer to capture long-range crack context, a Tokenized Kolmogorov-Arnold Network module to strengthen nonlinear feature representation, and Swin-based decoding to improve crack continuity recovery,” explains co-corresponding author

Da Shi. “Focal Loss is further adopted to address the imbalance between crack and background pixels and reduce false negatives.”

Validated on an integrated dataset of 10,422 images from public crack datasets and metro shield-tunnel images from Ningbo and Shanghai, CSwinUKAN outperformed U-Net, Swin-U-Net, U-KAN, and YOLO-based baselines, achieving stronger segmentation performance and recall. 

“This provides a promising approach for high-accuracy tunnel crack inspection and lays a foundation for future lightweight and real-time deployment,” says co-correspond author Xuguang Wang.

The researchers encouraged subsequent research to focus on optimizing computational efficiency. While the computational demand of the current model is feasible for static image segmentation utilizing contemporary industrial-grade GPUs, it spells a functional limitation for seamless integration into fully automated, real-time diagnostic systems.

Contact the author: 

Xuguang Wang, University of Hong Kong, xuguangw@hku.hk.

Funder:

Supported by the University of Hong Kong Start-up Fund for New Staff.

Conflict of interest:

All authors declare that there are no competing interests.

See the article:

Wong, Tsz Fan, Pak Ho Anson Wu, Yu Zhao, Da Shi, Xuguang Wang, and Yanliang Du. "CSwinUKAN: An enhanced U-Net architecture with swin-transformer and Kolmogorov-Arnold network modification for tunnel lining cracks segmentation." Journal of Railway Science and Technology (2026). https://doi.org/10.1016/j.jrst.2026.04.001