DSNet: A Computer Vision-Based Detection and Corrosion Segmentation Network for Corroded Bolt Detection in Tunnel

Abstract

Corroded bolt detection has been confirmed as a major issue in the structure health monitoring (SHM) of tunnels. However, detection-only methods will miss the corroded bolts, arising from the small rust area. In this study, the task is divided ingeniously into two parallel tasks, i.e., bolt detection and pixel-level rust segmentation, and the objective is fulfilled by taking the intersection of the two tasks, with the aim of enhancing the performance. To be specific, a detection and segmentation network (DSNet) is proposed based on multitask learning, leading to reduced false and missed detection rates. The coordinate attention module enhancing the focus of bolts in tunnel patches is incorporated in the detection branch, and the cross-stage partial-based decoder which can more accurately determine whether a pixel pertains to the corrosion area is employed in the segmentation branch. The mentioned branches share the same backbone to simplify the model. Sufficient comparisons and ablation experiments are performed to prove the superiority of the proposed algorithm based on the corroded bolt dataset captured from a real subway tunnel, which is publicly available in https://github.com/StreamHXX/Tunnel-lining-disease-image.