Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 545-549, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/545/2016/
doi:10.5194/isprs-archives-XLI-B1-545-2016
 
03 Jun 2016
A FEASIBILITY STUDY ON USE OF GENERIC MOBILE LASER SCANNING SYSTEM FOR DETECTING ASPHALT PAVEMENT CRACKS
Xinqu Chen and Jonathan Li Mobile Sensing and Geodata Science Lab, Department of Geography and Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
Keywords: Mobile Laser Scanning, Point Cloud, Pavement Crack, Automated Detection, Urban Road Abstract. This study aims to automatically detect pavement cracks on urban roads by employing the 3D point clouds acquired by a mobile laser scanning (MLS) system. Our method consists of four steps: ground point filtering, high-pass convolution, matched filtering, and noise removal. First, a voxel-based upward growing method is applied to construct Digital Terrain Model (DTM) of the road surface. Then, a high-pass filter convolutes the DTM to detect local elevation changes that may embed cracking information. Next, a two-step matched filter is applied to extract crack features. Lastly, a noise removal process is conducted to refine the results. Instead of using MLS intensity, this study takes advantages of the MLS elevation information to perform automated crack detection from large-volume, mixed-density, unstructured MLS point clouds. Four types of cracks including longitudinal, transvers, random, and alligator cracks are detected. Our results demonstrated that the proposed method works well with the RIEGL VMX-450 point clouds and can detect cracks in moderate-to-severe severity (13 - 25 mm) within a 200 m by 30 m urban road segment located in Kingston, Ontario, at one time. Due to the resolution capability, small cracks with slight severity remain unclear in the MLS point cloud.
Conference paper (PDF, 1616 KB)


Citation: Chen, X. and Li, J.: A FEASIBILITY STUDY ON USE OF GENERIC MOBILE LASER SCANNING SYSTEM FOR DETECTING ASPHALT PAVEMENT CRACKS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 545-549, doi:10.5194/isprs-archives-XLI-B1-545-2016, 2016.

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