# An Adaptive Multitask Network for Detecting the Region of Water Leakage in Tunnels  
[PERSON]  
1 School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (S.L)  
[PERSON]  
1 School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (S.L)  
[PERSON]  
1 School of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (S.L)  
[PERSON]  
2 School of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; [EMAIL_ADDRESS] (J.  
task as a pixel-by-pixel classification task. For example, [PERSON], [PERSON] et al. proposed a new formulation of deep Convolutional Neural Networks to implement segmentation tasks [12]. However, the method of pixel-by-pixel segmentation can't achieve structured predictions. The advent of FCN [13] solved the problem of structured prediction. [PERSON] et al. adopted the FCN network to achieve segmentation and identification of tunnel leakage diseases [14]. Various algorithms were proposed based on the FCN algorithm, including Deeplab series algorithms [15; 16; 17; 18], Segnet [19], U-Net [20], U-Net++ [21], and Unet3+ [22]. [PERSON] et al. proposed a new method that integrates the basic SegNet with a focal loss function. This method can accurately predict small cracks and structural damage in the tunnel [23]. [PERSON] et al. proposed CrackUnet, an improved automatic detected crack algorithm based on U-Net, and studied the impact of dataset size and model depth on training time, detection accuracy, and speed [24]. [PERSON] et al. used the U-Net++ model to perform pixel-level semantic segmentation of crack images [25]. [PERSON] et al. improved the deeplabv3+ algorithm to detect water leakage in subway tunnels and added an ECA-Net channel attention mechanism to two adequate feature layers in the codec part of the DeeplabV3+ model, resulting in an effective improvement in detection accuracy [26]. These algorithms work very well, and they're very trendy right now. However, water leakage detection is challenging. Water leakage detection is currently facing two significant challenges. 1. The background of the leakage image is complex, which is greatly affected by the actual project. 2. Due to the similar structure of water seepage and wet stains, and affected by illumination and shooting Angle, it isn't easy to detect the edge area. The algorithms of FCN and U-Net et al. adopt convolution. The lack of a global perception field leads to information loss during water leakage characteristic information transmission. These algorithms are not very effective in an environment with complex water leakage. Some studies have proposed that Vision Transformer (ViT) can solve the problem [27]. [PERSON] et al. used a ViT as the backbone network to segment the crack disease in the tunnel, which achieved good results [28]. Although the transformer has a natural global receptive field to solve this problem, it has limitations, such as being computationally intensive and the size of the test dataset being comparable to the size of the training dataset. In addition, to solve the problems of unclear edge detection, [PERSON] et al. tried to split the labels into complex and simple labels [29]. It turned out that using two decoders trained separately would lead to an over-reliance on hand-designed tags, so they are inefficient and not suitable for end-to-end networks. To solve the above problems, we designed the following network:  
1. Due to the complex background of the water leakage image, the information loss problem easily occurs during detection. To solve this problem, the encoder designed in this paper adopted a multilevel transformer and used depth separable convolution to mark the location information.