Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data

Recent advancements in automated tunnel defect detection have utilized high-resolution cameras and mobile laser scanners. However, the inability of cameras to accurately capture 3D spatial coordinates complicates tasks such as 3D visualization, while the relatively low resolution of laser scanners makes it difficult to detect small defects such as microcracks. In this paper, a comprehensive inspection method is proposed to address these limitations by integrating multi-defect detection, 3D coordinate acquisition, and visualization. The inspection process involves the capture of both image data and point cloud data of tunnel linings using the newly developed inspection cart (MTI-300). The proposed fusion approach combines image and point cloud data, leveraging the enhanced YOLOv8-seg instance segmentation model for defect identification. The scale-invariant feature transform (SIFT) algorithm is used to match local defect regions in the image data with the corresponding point cloud data, enabling the extraction of 3D coordinates and the integration of defect pixels with the point cloud information. Subsequently, a lightweight 3D reconstruction model is developed to visualize the entire tunnel and its defects using the fused data. The performance of the proposed method is validated and substantiated through a field experiment on Metro Line 8 in Qingdao, China.