A Tunnel Secondary Lining Leakage Recognition Model Based on an Improved TransUNet

Manual inspection methods traditionally used for tunnel lining leakage suffer from high subjectivity and low efficiency. At the same time, existing detection models do not perform well in terms of accuracy when faced with complex scenarios; this makes it essential to develop an intelligent leakage identification model that can adjust to varying background conditions. This study integrates a Vision Transformer (ViT) into UNet and constructs CBAM-TransUNet by embedding CBAMs into skip connections between the encoder-decoder structure and ViT outputs. Ablation experiments validate the efficacy of the CBAM and the ViT, while Score-CAM heatmaps analyze the model’s attention mechanism toward leakage features. The research results are as follows: (1) CBAM-TransUNet achieves average performance across metrics: 0.8143 (IoU), 0.8433 (Dice), 0.9518 (recall), 0.8482 (precision), 0.9837 (accuracy),0.9746 (AUC), 0.8568 (MCC), and 0.8970 (F1-score). These results indicate that the model performs excellently, even with dent shadows, stain interference, or faint traces. (2) Ablation experiments validate the pivotal roles of the CBAM and the ViT module: the IoU of the baseline model is 6.10% higher than that of the variant without the CBAM and 7.79% higher than that of the variant with both modules removed. (3) Through Score-CAM heatmap analysis, it is observed that the CBAM broadens the model’s attention coverage over leakage regions, strengthens feature continuity, and consequently enhances the model’s anti-interference performance in complex environments. This research could provide valuable reference insights for related fields.