# An Adaptive Multitask Network for Detecting the Region of Water Leakage in Tunnels
## 5 Results
### Ablation Study  
Because of the need to divide labels manually for some traditional models, the adaptive multitask network proposed in this paper is intended to avoid the use of manual partition labels for satisfactory results. However, threshold R is required by the adaptive multitask decoder proposed in this paper, which is reasonable in the range of 0.5-1. To identify the value in the scope of 0.5-1 that can make the model perform best, a lot of experiments have been conducted on the basis of empiricism. The experimental results are shown in Figure 14, where the blue polyline represents the change that occurs to Dice.  
When the model takes different thresholds, the red polyline indicates the change in _MIOU_. In the experiment, the ablation study threshold is selected from 0.5 to 1, which leads to the optimal peak in both polylines at around 0.75. Additionally, Table 6 shows the alterations to evaluation metrics at the threshold values of 0.5, 0.75, and 0.9. According to the table, _MIOU_, _Dice_, _PA_, and _F1_ reach their peak values when the threshold is set to 0.75.  
This paper conducts the ablation study to prove that the decoder composed of the adaptive multitask network branches performs better than the decoder composed of a single-task network. Figure 15 obtains the visual segmentation results. As can be seen from the figure, compared with the single-task network, the water leakage image divided by the adaptive multitask decoder is more precise and has more edge information. Visual results prove that the multitask decoder performs better than the single task.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**R** & **MIOU** & **Dice** & **PA** & **F1** \\ \hline
0.5 & 0.822 & 0.852 & 0.937 & 0.931 \\
0.75 & 0.904 & 0.951 & 0.983 & 0.947 \\
0.9 & 0.833 & 0.862 & 0.943 & 0.926 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Threshold R experimental setup.  
Figure 14: The changing polylines of MIOU and Dice when the model adopted different thresholds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **F1** & **PA** & **MIOU** & **FWIOU** & **Dice** \\ \hline Ours & 0.947 & 0.983 & 0.904 & 0.971 & 0.951 \\ TransUNet & 0.916 & 0.961 & 0.858 & 0.936 & 0.915 \\ UNet++ & 0.801 & 0.791 & 0.662 & 0.833 & 0.794 \\ UNet++ & 0.848 & 0.834 & 0.725 & 0.856 & 0.844 \\ DeepLabv3+ & 0.785 & 0.784 & 0.821 & 0.832 & 0.756 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Segmentation results of different methods on the water leakage dataset.  
Evaluation indexes are introduced to prove that the multitask decoder performs better than the single-task decoder. Tables 7 and 8, respectively, show the specific value of the evaluation index. These results presented in the table indicate that the multitask network exhibits superior performance compared to the single-task network for both the water seepage and wet stain regions. _PA_ of the adaptive multitask network is 95.8, higher than the single-task segmentation network, which segments the water seepage area. Additionally, the _MIOU_ of the model is 88.4%, higher than the single-task segmentation network, which segments the area of the wet stains. These results demonstrate that the model in the paper can effectively enhance the segmentation accuracy of water leakage in tunnels.