# Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels
## 5 Conclusions  
In this paper, an HSL approach for the automatic segmentation of water leakage images is proposed. The water-leakage labels are automatically generated by WL-CAM based on the weakly supervised method. The water leakage is semantically segmented based on the fully supervised method WRDeepLabV3+.  
In WL-CAM, according to the characteristics of water leakage, an adaptive APS algorithm is proposed, which can accurately and completely segment the water leakage area and generate image-level labels. Secondly, a weakly supervised network WRes2 Net is proposed to generate CAM labels. The CAM label files overlap with image-level labels and pixel values and are fused to generate pixel-level labels, which further improves label accuracy and saves manual labeling costs.  
In WRDeepLabV3+, the WRes2 Net in WL-CAM is used as the core network of WRDeepLabV3+. WRDeepLabV3+ based on a fully supervised network is proposed, and the parameters and framework are adjusted to improve the performance. The excellent robustness of WRDeepLabV3+ is verified by semantic segmentation of water leakage images with different complex noise information.  
WL-CAM and WRDeepLabV3+ were tested and compared with other state-of-the-art semantic segmentation methods on the proposed dataset. The results show that WL-CAM has better performance of automatically generating labels than other methods, with a difference of up to 12.1% in the evaluation indicators. All performance indicators of WRDeepLabV3+ are also ahead of other advanced fully supervised methods.  
The HSL automatic segmentation approach for water leakage images performs on par with WRDeepLabV3+ and the manual labeling dataset, validating the feasibility and accuracy of WL-CAM. Due to the excellent performance of WL-CAM and WRDeepLabV3+, the hybrid-supervision approach achieves an _MloU_ of 82.8% on the dataset, which is 6.4% higher than the second end-to-end approach. The efficiency is 25% higher than that of the second. In a complex subway tunnel environment, it can accurately segment the water leakage area.  
In the future, more images of water leakage in subway tunnels will be collected as the training dataset to improve the segmentation accuracy and performance of the HSL approach. At the same time, the weakly supervised learning method will be optimized to reduce the complexity of the method and save the training time of the dataset.  
Figure 10: The performance effect of transfer learning on WRDeepLabV3+.  
**Author Contributions:** D.Q.: the conception and design of the work, data analysis, problem modeling. H.L.: data acquisition, problem modeling. Z.W.:data analysis, problem modeling. Y.T.: data acquisition, problem modeling. S.W.: the conception and design of the work, methodology, problem modeling, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The National Natural Science Foundation of China (No.61902016), the postgraduate education and teaching quality improvement project of BUCEA, China (No.J2022005), the BUCEA Post Graduate Innovation Project (No.PG2022118).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.