# An Adaptive Multitask Network for Detecting the Region of Water Leakage in Tunnels
## 5 Results
### Compared with SOTA Methods  
To provide a clear understanding of the segmentation effect of each model on the water leakage images in tunnels, we compared different algorithms, which include TransUNet [36], U-Net++, U-Net++, and Deeplabv3+. Figure 13 illustrates some results from the different algorithms.  
Figure 13a shows the segmentation results of water leakage images under the background of a pipeline, light, joint, and bolt hole. Even under light solid interference and pipeline occlusion, the model in this paper can still maintain a good segmentation effect and accurately identify the shape of tunnel leakage. In the pipeline, lights, and tunnel background in Figure 13b, there are pipes, bolt holes, and other materials that resemble the appearance and color of the leak. The model in this article rarely mistakes the interference in the background for a leak. However, the other four models have varying degrees of misclassification. In the image background of Figure 13c, there are complex backgrounds such as separate water seepage areas, joint and bolt holes, etc. The segmentation results of the model in this paper do not connect the water seepage area. However, the other four  
Figure 12: The variation curve of loss in training and verification loss with or without data enhancement. (**a**) The variation curve of loss in training and verification loss without data enhancement; (**b**) The variation curve of loss in training and verification loss with data enhancement.  
models have different faults in segmentation. The segmentation results of the network model in this paper are relatively good for the water leakage images with relatively complex environmental conditions, and most of the water leakage region can be segmented from the background. The above results showed that, compared with other model algorithms, the model in this paper has more refined segmentation of edge contours and has better anti-interference ability, which can realize feature recognition under complex background and lighting conditions.  
This paper tests the properties of the five network models to illustrate the performance of the model by using the same test dataset. Table 5 shows the performance indicators results. The \(PA\) value of the model in this paper is 98.3%, which is 2.2%, 19.2%, 14.9%, and 10.9% higher than those of TransUNet, UNet++, UNet++, and DeepLabv3+ models, respectively. The \(MIOU\) of the model in this paper is 90.4%, which is 4.6%, 24.2%, 17.9%, and 8.3% higher than those of TransUNet, UNet++, UNet++, and DeepLabv3+ models, respectively. To test whether the complexity of the model decreases after the introduction of attenuation ratio R in the self-attention layer calculation. A comparative experiment is conducted in this study on the number of parameters between the adaptive multitask network and TransUNet with ViT as the encoder. The number of parameters of TransUNet is 726 M, but that of the adaptive multitasking network is 73.9 M, which is about one-10 th of that of TransUNet. Therefore, the adaptive multitask network model yields a higher efficiency and a better performance, with the effectiveness of the attenuation ratio R confirmed. The above results show that the algorithm model proposed in this paper can effectively improve the segmentation accuracy of the water leakage in the tunnel lining. Compared with the traditional semantic segmentation algorithm, the semantic segmentation algorithm proposed in this paper has higher accuracy in identifying tunnel water leakage. It can accurately locate the location of different types of tunnel leakage water to detect leakage water and the pixel-level segmentation of the shape.  
Figure 13: Visual results of different algorithms. (**a–e**) Original images; (**a1–e1**) Ground truth; (**a2–e2**) proposed method; (**a3–e3**) TransUNet; (**a4–e4**) UNet++; (**a5–e5**) UNet++; (**a6–e6**) Deeeplabv3+.