## 8 Conclusions  
1. Based on the SE channel attention mechanism, an SE-TransUNet model was proposed for tunnel lining leakage detection. Trained on a constructed hybrid leakage dataset, the model achieved average values of 0.8318 (IoU), 0.8304 (Dice), 0.9394 (Recall), 0.8480 (Precision), 0.9733 (AUC), 0.8562 (MCC), 0.9218 (F1-score), and 6.53 FPS. The model demonstrated strong generalization and robustness under challenging conditions, including shadowed indentations on the lining surface, surface contamination such as white spots and stains, shallow leakage marks with low contrast against the background, and partial occlusions.
2. Ablation experiments were conducted by progressively removing the SE-Block and ViT modules from the SE-TransUNet model. All evaluation metrics declined to varying extents. The variant with all SE-Blocks and the ViT module removed showed a substantial performance gap from SE-TransUNet, with an IoU difference of up to 7.04%.
3. Score-CAM heatmap analysis across different leakage patterns revealed that SE-TransUNet performed consistently well in detecting area-type, linear, spot-type, and mixed-form tunnel leakage. Compared with ablation variants, its heatmaps provided more complete coverage of the leakage regions, with high-activation areas more closely aligned with the true leakage boundaries and exhibiting stronger spatial continuity, thereby enabling more accurate characterization of leakage spatial distributions across diverse forms.  
Although the SE-TransUNet model demonstrated relatively high accuracy in leakage detection, this study still has several limitations in terms of dataset and annotation quality, computational demands and real-time performance, model interpretability and engineering applicability, as well as experimental design. These limitations will be addressed in future work.  
Conceptualization, S.S. and H.W.; methodology, R.S., and L.W.; software, R.S.; validation, S.S. and H.W.; formal analysis, S.S. and H.W.; investigation, Y.W. and L.W.; resources, R.S.; data curation, S.S., H.W., and Y.W.; writing--original draft preparation, R.S.; writing--review andediting, R.S.; visualization, R.S.; supervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W. and L.W. All authors have read and agreed to the published version of the manuscript.
* [1] This research was supported by Shandong Provincial Communications Planning and Design Institute Group Co., Ltd. through the Shandong Provincial Enterprise Technology Innovation Program (Grant No. 2024537010000680) and the Science and Technology Project of Shandong Provincial Communications Planning and Design Institute Group Co., Ltd. (Grant No. KJ-2023-SYJT-16).
* [2] Not applicable.
* [3] Not applicable.
* [4] Data Availability Statement: Data are available from the corresponding author upon reasonable request.  
The authors express special thanks to the editors and anonymous reviewers for their constructive comments.  
Author [PERSON] was employed by the company Shandong Provincial Communications Planning and Design Institute Group Co., Ltd. The authors declare that this study received funding from Shandong Provincial Communications Planning and Design Institute Group Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.