## 6 Ablation Experiments
### Analysis of Heatmaps from Ablation Experiments  
Score-CAM [31] is a heatmap generation technique used for neural network visualization, aiming to accurately localize the regions in an image that contribute most to the model's prediction, without relying on gradient information from backpropagation. Compared to traditional Class Activation Mapping (CAM) methods, Score-CAM offers the advantage of replacing gradient-based weighting with a combination of activation maps, thereby reducing dependency on feature gradients and enhancing the stability and interpretability of the visualization results [32]. The computation principle is defined in Equation (16):  
\[\left\{ \begin{aligned} & H_{l}^{k}=\sigma\Big{(}UP\Big{(}A_{l}^{k} \Big{)}\Big{)}\\ & C_{l}^{k}=f\Big{(}X\cdot H_{l}^{k}\Big{)}-f(X)\end{aligned}  \right.. \tag{16}\]  
where \(A_{l}^{k}\) represents the size of the output feature map; l represents the output feature level; k represents the number of channels corresponding to each feature layer; \(\sigma(\cdot)\) is the sigmoid activation function, which is used to normalize the value interval of the feature map; \(H_{l}^{k}\) represents the size of the original output image; \(f(X)\) is the input feature map, and \(f(X\cdot H_{l}^{k})\) is the weighted result of the input feature map. \(C_{l}^{k}\) is the attention area of the model for the input image. Finally, the Score-CAM heatmap of the baseline SE-TransUNet model is presented in Figure 11. The values within the white boxes indicate the corresponding IoU scores. In the heatmap, regions with more intense red coloration represent areas that the model identifies as contributing more significantly to the prediction, indicating higher attention. (1) Heatmap Analysis of Image 1: Area-Type Leakage  
This scene involves area-type leakage at the arch-waist region of the tunnel, where the leakage appears as irregular patches with blurred boundaries against the surrounding damp concrete. The background also contains distinct construction textures. The baseline SE-TransUNet model achieved an IoU of 0.95. In the heatmap, the leakage area is continuously covered by red and dark yellow regions, and the edges align well with the ground truth, indicating the model's ability to distinguish leakage from damp background areas. After removing SE-Block components, the IoU of the variant models decreased. In particular, when SE-Block3 was removed, the high-confidence regions in the heatmap shrank to light green. When the ViT module was removed, the IoU dropped to 0.82, and evident omissions were observed along the leakage boundaries in the heatmap. (2) Heatmap Analysis of Image 2: Spot-Type Leakage  
This scenario contains three discrete spot-type leakages, each approximately 2-5 cm in diameter. The grayscale contrast between the leakage spots and the surrounding concrete is low, and the background includes surface defects such as honeycombs, which may interfere with detection. The SE-TransUNet model achieved an IoU of 0.94. Each leakage spot is clearly enclosed by a distinct dark yellow region in the heatmap, with well-defined boundaries and minimal background interference, indicating the model's ability to focus on small-scale leakage features. After removing SE-Block modules, the IoU decreased to 0.89, and the middle leakage spot's yellow activation region disappeared. When the ViT module was removed, the IoU further dropped to 0.86, and the leakage regions in the heatmap appeared with blurred edges. (3) Heatmap Analysis of Image 3: Linear Leakage  
This scenario involves linear leakage distributed along a concrete joint. The leakage path is narrow and continuous, with rough textures present on both sides of the joint, increasing the risk of confusion with non-leakage joints. The SE-TransUNet model achieved an IoU of 0.83. In the heatmap, the linear leakage is visualized as a continuous dark yellow strip, maintaining high confidence even at joint bends. After removing all SE-Block modules, the IoU decreased to 0.78.