Detection of Distress Region from Subway Tunnel Images via U-net-based Deep Semantic Segmentation

摘要

The maintenance and management of old infrastructures has become an urgent issue nowadays, as an important part of transportation infrastructures, the subway tunnels are facing the same problem as other infrastructures. In this paper, we present a distress detection method in subway tunnels using U-net. As one of the semantic segmentation neural networks, U-net shows promising performance in remote sensing image segmentation and medical image segmentation. We apply this network to a subway tunnel dataset and compare it with other three semantic segmentation methods. From our experiment, it is shown that the proposed approach achieves promising results in our task.