Affiliations: [a]
School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi, China
| [b]
School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi, China
Correspondence:
[*]
Corresponding author. Liang Zhao, School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, China. E-mail: zhaoliang20220824@163.com.
Abstract: Tunnels water leakage detection in complex environments is difficult to detect the edge information due to the structural similarity between the region of water seepage and wet stains. In order to address the issue, this study proposes a model comprising a multilevel transformer encoder and an adaptive multitask decoder. The multilevel transformer encoder is a layered transformer to extract the multilevel characteristics of water leakage information, and the adaptive multitask decoder comprises the adaptive network branches. The adaptive network branches generate the ground truths of wet stains and water seepage through the threshold value and transmit them to the network for training. The converged network, the U-net, fuses coarse images from the adaptive multitask decoder, and the fusion images are the final segmentation results of water leakage in tunnels. The experimental results indicate that the proposed model achieves 95.1% Dice and 90.4% MIOU, respectively. This proposed model demonstrates a superior level of precision and generalization when compared to other related models.
