# Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels
## 3 Experiment
### Experimental Results
For water leakage with stain interference, redundant background information appears in the EM and 1 Stage segmentation regions. However, in the first four approaches, the outline of the segmentation area is lacking or overfitting. The approach in this paper segmented the region with a more refined and complete outline. For the occluded water leakage, when \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Noise Information** & **Iron Tubes** & **Cables** & **Iron Wires** & **Electricity Meters** & **Stains** \\ \hline The original images & & & & & & & \\ Ground truths & & & & & & & \\ \hline WL-CAM & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Water leakage segmentation effects of WL-CAM and WRDeepLabV3+ on different noises. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method Categories** & **Methods** & **_Loss_ & **\begin{tabular}{c} **MIoI** \\ **(\%)** \\ \end{tabular}** & **\
\begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular}** & **F1 (\%)** & **
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular}** & **Epoch** & **Training Time** & **Testing Time** \\ \hline Weakly & SC-CAM & 0.154 & 69.1 & 63.5 & 70.2 & 78.7 & 50 & 18 h 58 m 07 s & 0.087 s/image \\ supervised learning & WL-CAM & **0.132** & **73.6** & **75.6** & **79.5** & **83.9** & 50 & **15 h 21m 16 s** & **0.061 s/image** \\ \hline \multirow{4}{*}{Fully supervised learning} & PSPNet & 0.096 & 80.4 & 82.8 & 80.1 & 77.6 & 100 & **11 h 04 m 46 s** & **0.028 s/image** \\  & U-Net & 0.077 & 82.0 & 83.8 & 81.9 & 80.1 & 100 & 17 h 55 m 59 s & 0.074 s/image \\ \cline{1-1}  & HRNet & 0.089 & 81.0 & 83.3 & 80.8 & 78.6 & 100 & 19 h 57 m 58 s & 0.095 s/image \\ \cline{1-1}  & DeepLabV3+ & 0.079 & 81.8 & 85.2 & 81.8 & 78.7 & 100 & 11 h 53 m 34 s & 0.033 s/image \\ \cline{1-1}  & WRDeepLabV3+ & **0.053** & **86.2** & **88.6** & **86.7** & **84.9** & 100 & 13 h 22 m 33 s & 0.035 s/image \\ \hline
**HSL (proposed)** & 0.084 & 82.8 & 85.2 & 82.9 & 80.6 & 100 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of evaluation results of different semantic segmentation under the proposed dataset. the occluded part cannot be known, the five approaches all show good results, but EM still has the problem of incomplete segmentation. As can be seen from Table 9, the _MloU_ of the approach proposed in this paper outperforms other end-to-end semantic segmentation networks on both the Val dataset and the Test dataset, reaching 81.7% and 82.8%, respectively. HSL is also the most efficient, 25% higher than the second. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approaches** & **Backbone** & **Val (\%)** & **Test (\%)** & **Testing time** \\ \hline EM & VGG16 & 58.4 & 59.8 & 0.092 s/image \\ CRF-RNN & VGG16 & 61.6 & 62.9 & 0.068 s/image \\
1 Stage & WideResNet38 & 73.4 & 74.1 & 0.044 s/image \\ AA\&LR & WideResNet38 & 75.6 & 76.4 & 0.052 s/image \\
**HSL** & WRes2 Net101 & **81.7** & **82.8** & **0.035 s/image** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _MloU_ and the efficiency comparison between the HSL and the end-to-end semantic segmentation network on the proposed dataset.