3D laser imaging and sparse points grouping for pavement crack detection

Traditional optical imaging has limitations in capturing and representing pavement cracks due to the impact of illumination variations and cast shadows. In this work, laser-imaging techniques are employed to model the pavement surface with dense 3D points, and a sparse points grouping method is proposed to detect cracks from the 3D point clouds. Firstly, an algorithm based on frequency analysis is presented to separate potential cracks from the control profile and material texture of the pavement. Secondly, range images generated from point clouds are partitioned into image patches, and a learning algorithm is used to identify image patches probably containing cracks. Thirdly, the extracted patches are further filtered by checking the consistency of potential crack directions. Finally, edge weights are assigned to crack seed pairs by referring to the Gestalt law, and minimum spanning tree based algorithms are developed to extract the final cracks. Extensive experiments demonstrate the effective of the proposed method.