Combining Cylindrical Voxel and Mask R-CNN for Automatic Detection of Water Leakages in Shield Tunnel Point Clouds

Abstract

Water leakages can affect the safety and durability of shield tunnels, so rapid and accurate identification and diagnosis are urgently needed. However, current leakage detection methods are mostly based on mobile LiDAR data, making it challenging to detect leakage damage in both mobile and terrestrial LiDAR data simultaneously, and the detection results are not intuitive. Therefore, an integrated cylindrical voxel and Mask R-CNN method for water leakage inspection is presented in this paper. This method includes the following three steps: (1) a 3D cylindrical-voxel data organization structure is constructed to transform the tunnel point cloud from disordered to ordered and achieve the projection of a 3D point cloud to a 2D image; (2) automated leakage segmentation and localization is carried out via Mask R-CNN; (3) the segmentation results of water leakage are mapped back to the 3D point cloud based on a cylindrical-voxel structure of shield tunnel point cloud, achieving the expression of water leakage disease in 3D space. The proposed approach can efficiently detect water leakage and leakage not only in mobile laser point cloud data but also in ground laser point cloud data, especially in processing its curved parts. Additionally, it achieves the visualization of water leakage in shield tunnels in 3D space, making the water leakage results more intuitive. Experimental validation is conducted based on the MLS and TLS point cloud data collected in Nanjing and Suzhou, respectively. Compared with the current commonly used detection method, which combines cylindrical projection and Mask R-CNN, the proposed method can achieve water leakage detection and 3D visualization in different tunnel scenarios, and the accuracy of water leakage detection of the method in this paper has improved by nearly 10%.