## 2 SE-TransUNet Water Seepage Detection Model
### Decoder of Water Seepage Recognition Model  
The decoder part of the model mainly includes upsampling layers, SENet channel attention modules, and VGGBlock convolution blocks. The upsampling layer uses bilinear interpolation (scale_factor = 2) to gradually enlarge the size of the feature map. The SENet module is used to suppress redundant channels and enhance key semantic channels. The VGGBlock convolution block is used to fuse the spliced multi-scale features and extract local details. Assuming that the four-level feature layers output by the encoder are \(x_{1}\) (output by encoder1), \(x_{2}\) (output by encoder2), \(x_{3}\) (output by encoder3), and \(x_{\text{vit}}\) (output by ViT), the specific data processing flow is shown in Table 3.  
### Decoder of Water Seepage Recognition Model  
The decoder part of the model mainly includes upsampling layers, SENet channel attention modules, and VGGBlock convolution blocks. The upsampling layer uses bilinear interpolation (scale_factor = 2) to gradually enlarge the size of the feature map. The SENet module is used to suppress redundant channels and enhance key semantic channels. The VGGBlock convolution block is used to fuse the spliced multi-scale features and extract local details. Assuming that the four-level feature layers output by the encoder are \(x_{1}\) (output by encoder1), \(x_{2}\) (output by encoder2), \(x_{3}\) (output by encoder3), and \(x_{\text{vit}}\) (output by ViT), the specific data processing flow is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module Name** & **Input Size** & **Output Size** & **Number of Channels** & **Feature Type** \\ \hline encoder1 & \(512\times 512\times 3\) & \(256\times 256\times 64\) & 64 & Edges, Textures (Low-Level Features) \\ encoder2 & \(256\times 256\times 64\) & \(128\times 128\times 256\) & 256 & Features \\ encoder3 & \(128\times 128\times 256\) & \(64\times 64\times 512\) & 512 & Object Parts (Mid-Level Features) \\ encoder4 & \(64\times 64\times 512\) & \(32\times 32\times 1024\) & 1024 & Holistic Semantics (High-Level Features) \\ ViT & \(32\times 32\times 1024\) & \(16\times 16\times 1024\) & 1024 & Global Semantic Enhanced Features \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input and output dimensions and feature types of modules in the encoder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Process Number** & **Process Function** & **Process Explanation** \\ \hline
1 & Input Feature Map & The output feature size of ResNet needs to match the input feature size of ViT. If they do not match, additional upsampling is required \\ \hline
2 & Patch Partitioning & Each patch has a size of \(8\times 8\). If the output feature map of encoder4 is \(32\times 32\), then 16 patches are partitioned \\ \hline
3 & Global Semantic Enhancement & Based on the self-attention mechanism of Transformer, long-range dependencies among 16 patches are captured \\ \hline
4 & Resolution Adaptation & Transform the feature size to reduce the resolution difference with the shallow features in the decoder, facilitating subsequent splicing and fusion \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data processing flow of the ViT module in the leakage identification model encoder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Process Number** & **Process Function** & **Process Explanation** \\ \hline
1 & Input Feature and Attention Enhancement & The decoder first applies SENet attention to each feature layer. \\ \hline
2 & Layer 1 Decoding & Upsample \(x_{\text{vit}}\), and splice and fuse the upsampled \(x_{\text{vit}}\) with \(x_{3}\) through VGGBlock. \\ \hline
3 & Layer 2 Decoding & Upsample the output of the first layer decoding, and splice and fuse it with \(x_{2}\) through VGGBlock. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data processing flow of the decoder in the leakage identification model.