ACPA-Net: Atrous Channel Pyramid Attention Network for Segmentation of Leakage in Rail Tunnel Linings

Abstract

The automatic segmentation of leakage in rail tunnel linings is a useful and challenging task. Unlike other scenarios, the complex environment inside the tunnels makes it difficult to obtain accurate results for the segmentation of leakages. Some deep learning-based methods have been used to automatically segment tunnel leakage, but these methods ignore the interdependencies between feature channels, which are very important for extracting robust leakage feature representations. In this work, we propose an atrous channel pyramid attention network (ACPA-Net) for rail tunnel lining leakage segmentation. In ACPA-Net, the proposed atrous channel pyramid attention (ACPA) module is added into a U-shaped segmentation network. The ACPA module can effectively strengthen the representation ability of ACPA-Net by explicitly modeling the dependencies between feature channels. In addition, a deep supervision strategy that helps ACPA-Net improve its discrimination ability has also been introduced into ACPA-Net. A rail tunnel leakage image dataset consisting of 1370 images with manual annotation maps is built to verify the effectiveness of ACPA-Net. The final experiment shows that ACPA-Net achieves state-of-the-art performance on the Crack500 dataset and our rail tunnel leakage image dataset, and our method has the least number of parameters of all the methods.