# Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels
## 3 Experiment
### Experimental Results  
The segmentation results of APS are compared with several other image-segmentation algorithms (as shown in Table 4). K-means and Otus have more background information than global threshold segmentation (threshold is 90). For the water leakage area, the first three segmentation algorithms all have the phenomenon of incomplete segmentation, and independent pixels appear. Compared with the three segmentation algorithms, APS can better segment the water leakage area. The background information is effectively removed while retaining the water leakage area. A comparison of the labeling effect of ground truths and AL-Mask images is shown in Table 5. The first column shows the original dataset of water leakage. The second column shows the water leakage dataset (ground truth) manually marked by Labelme. The third column shows the image-level water leakage dataset obtained by APS. The fourth column shows the mask data, and the fifth column shows the final pixel-level water leakage dataset (AL-Mask images). Compared with ground truth, the edge of the AL-Mask images is not smooth, but it can effectively extract the water leakage area. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Effects** & **K-Means** & **Otus** & **Threshold** & **APS** \\ \hline Overall effects & & & & \\ Water leakage details & & & & \\ \hline Background information details & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of image segmentation algorithms. The subway tunnel's surface environment is complex, and there are various types of noise, such as the occlusion of objects and the influence of artificial or oil stains, which cause certain difficulties in the segmentation of water leakage. As shown in Table 6, five typical noise images of water leakage of subway tunnels are listed, as well as their corresponding ground truth, WL-CAM, and WRDeepLabV3+ renderings. Under the complex background, WL-CAM has over-segmentation and incomplete segmentation, while WRDeepLabV3+ obtains a better segmentation effect. As shown in Table 7, the HSL approach proposed in this paper is compared with the current advanced semantic segmentation network. Since WL-CAM adopts two-stage training, a larger learning rate and batch size are used in the freezing stage, and a smaller learning rate and batch size are used in the thawing stage, which greatly shortens the training time. Compared with SC-CAM, WL-CAM is more time-saving, and all indicators are better than SC-CAM. In fully supervised learning, PSPNet has the highest efficiency, but the accuracy is sacrificed. WRDeepLabV3+ based on WRes2 Net101 optimizes the learning rate and adds an optimizer so that the first five evaluation indicators are ahead of other fully supervised networks. It can be seen that the HSL approach proposed in this paper can achieve comparable or even better performance than the existing automatic annotation method SC-CAM. Its segmentation accuracy can also be comparable to the full supervised network. Since the HSL approach includes multiple steps, other models only calculate the model training time, so the time used by this approach is not comparable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Original Images** & **Ground Truths** & **APS Images** & **Mask Data** & **AL-Mask Images** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the manually labeled dataset and the automatically labeled dataset. As shown in Table 8, the segmentation effects of the HSL approach and the end-to-end semantic segmentation network under different water leakage types are compared. It can be seen from this that, compared with block water leakage, EM, 1 Stage, and AA&LR have better segmentation results. They can identify small water leakage blocks. While the approach in this paper can more completely segment small water leakage blocks. For vertical water leakage, the EM segmentation is incomplete, and the CRF-RNN is over-fitting. For horizontal water leakage, the EM has the problem of excessive segmentation.