Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 717-723, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/717/2016/
doi:10.5194/isprs-archives-XLI-B1-717-2016
 
06 Jun 2016
RAPID INSPECTION OF PAVEMENT MARKINGS USING MOBILE LIDAR POINT CLOUDS
Haocheng Zhang1, Jonathan Li1,2, Ming Cheng2, and Cheng Wang2 1Mobile Mapping Lab, Department of Geography and Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
2Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Information Science and Engineering, Xiamen University, 422 Siming Road South, Xiamen, FJ 361005, China
Keywords: Mobile LiDAR, Point Cloud, Pavement Marking, Urban Road, Automated Detection Abstract. This study aims at building a robust semi-automated pavement marking extraction workflow based on the use of mobile LiDAR point clouds. The proposed workflow consists of three components: preprocessing, extraction, and classification. In preprocessing, the mobile LiDAR point clouds are converted into the radiometrically corrected intensity imagery of the road surface. Then the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu’s thresholding, neighbor-counting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters using a manually defined decision tree. Case studies are conducted using the mobile LiDAR dataset acquired in Xiamen (Fujian, China) with different road environments by the RIEGL VMX-450 system. The results demonstrated that the proposed workflow and our software tool can achieve 93% in completeness, 95% in correctness, and 94% in F-score when using Xiamen dataset.
Conference paper (PDF, 1423 KB)


Citation: Zhang, H., Li, J., Cheng, M., and Wang, C.: RAPID INSPECTION OF PAVEMENT MARKINGS USING MOBILE LIDAR POINT CLOUDS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 717-723, doi:10.5194/isprs-archives-XLI-B1-717-2016, 2016.

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