Automatic concrete spalling detection has become an important issue for metro tunnel examinations and maintenance. This paper focuses on concrete spalling detection research with surface roughness analysis based on point clouds produced by 3D mobile laser scanning (MLS) system. In the proposed method, at first, the points on ancillary facilities attached to tunnel surface are considered as outliers and removed via circular scan-line fitting and large residual error filtering. Then, a roughness descriptor for the metro tunnel surface is designed based on the triangulated grid derived from point clouds. The roughness descriptor is generally defined as the ratio of surface area to the projected area for a unit, which works well in identifying high rough areas on the tunnel surface, such as bolt holes, segment seams, and spalling patches. Finally, rough area classification based on Hough transformation and similarity analysis is performed on the identified areas to accurately label patches belonging to segment seams and bolt holes. After removing the patches of bolt holes and segment seams, the remaining patches are considered as belonging to concrete spalling. The experiment was conducted on a real tunnel interval in Shanghai. The result of concrete spalling detection revealed the validity and feasibility of the proposed method.

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