# Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels
## 3 Experiment
### Experimental Scheme  
Firstly, we demonstrate the excellent segmentation ability of APS in weakly supervised learning and the method's effectiveness in automatically generating water leakage labels. First, the original image dataset is segmented into image-level labels via APS. As the subway tunnels mostly have a gray and white background, it forms a strong color contrast with the water leakage. The images are converted to grayscale images, the water leakage area is dark in the grayscale images, and the grayscale value is low. Through a large number of analyses and statistics of the gray value distribution of the water leakage area, it is found that the gray value of the water leakage area is greater than 60 and less than 130, while the background area is greater than 130 [39,40]. Through global threshold segmentation, the background information with a gray value greater than 130 is filtered out and then segmented and compared with images of other image segmentation algorithms. So a is chosen as 60, and b is chosen as 130. Second, image-level labels for multi-class training are provided via WRes2 Net. CAM label files are generated for fusion with image-level labels. Redundant background information is removed, and AL-Mask images are generated and compared with the ground truth. After many experiments, the fusion weights \(\alpha\) and \(\beta\) are taken as 0.8 and 0.2, respectively.  
Secondly, the robustness of fully supervised learning WRDeepLabV3+ is demonstrated. Deep learning training on AL-Mask images is carried out using WRDeepLabV3+. Due to the complex environment of subway tunnels and different noise information from water leakage, the water leakage data are divided into five categories, and the semantic segmentation effect of WRDeepLabV3+ is displayed through different categories.  
Finally, the advanced nature and accuracy of the HSL approach are demonstrated. The performance of the approach combined with WL-CAM and WRDeepLabV3+ is compared with other advanced semantic segmentation networks, including SC-CAM [22], U-Net [41], PSPNet [42], HRNet [43], DeepLabV3+ [44], and WRDeepLabV3+. Because of the complex environment of ground tunnels, water leakage has the following three characteristics [45]. First, due to the lateral extrusion and gravity of the lining, as well as the influence of different lining gaps, the water leakage will occur on the lining surface, forming strip water leakage (including horizontal and vertical directions) and blocking water leakage. Second, there will be oil stains and artificial marks on the surface of the inner lining, which will be similar to the color and shape of the water leakage, affecting the segmentation accuracy. Third, the water leakage area will be covered by meter boxes, cables, pipes, etc., causing interference. The water leakage data are divided into five categories, using the HSL approach proposed in this paper and other end-to-end semantic segmentation networks including EM [46], CRF-RNN [47], 1 Stage [48], and AA&LR [49] for semantic segmentation comparison is also performed on the dataset.  
The experiments are comprehensively evaluated by using _Precision_, _Recall_, _IoU_, _MIoU_, and \(F\)1 to judge the performance of the model by researchers [50].  
\[Precision=\frac{TP}{TP+FP} \tag{16}\]  
\[Recall=\frac{TP}{TP+FN} \tag{17}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups** & **Train** & **Val** & **Test** & **Sum** \\ \hline A & 4000 & 1000 & 1000 & 6000 \\ B & 4000 & 1000 & 1000 & 6000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of the experimental dataset.  
\[MIoU=\left(\frac{TP}{TP+FP+FN}+\frac{TN}{TN+FN+FP}\right)/2 \tag{19}\]  
\[F1=\frac{2\times TP}{2\times TP+FP+FN} \tag{20}\]  
where _TP_: True Positive; _FP_: False Positive; _TN_: True Negative; _FN_: False Negative.