Title :
Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data
Author :
Haiyan Guan ; Li, Jie ; Yongtao Yu ; Chapman, M. ; Hanyun Wang ; Cheng Wang ; Ruifang Zhai
Author_Institution :
Dept. of Comput. Sci., Xiamen Univ., Xiamen, China
Abstract :
The assessment of pavement cracks is one of the essential tasks for road maintenance. This paper presents a novel framework, called ITVCrack, for automated crack extraction based on iterative tensor voting (ITV), from high-density point clouds collected by a mobile laser scanning system. The proposed ITVCrack comprises the following: 1) the preprocessing involving the separation of road points from nonroad points using vehicle trajectory data; 2) the generation of the georeferenced feature (GRF) image from the road points; and 3) the ITV-based crack extraction from the noisy GRF image, followed by an accurate delineation of the curvilinear cracks. Qualitatively, the method is applicable for pavement cracks with low contrast, low signal-to-noise ratio, and bad continuity. Besides the application to GRF images, the proposed framework demonstrates much better crack extraction performance when quantitatively compared to existing methods on synthetic data and pavement images.
Keywords :
crack detection; feature extraction; iterative methods; maintenance engineering; optical scanners; road building; roads; GRF image generation; ITV-based crack extraction; ITVCrack; automated crack extraction; curvilinear crack delineation; georeferenced feature image generation; high-density point clouds; iterative tensor voting; mobile laser scanning data; mobile laser scanning system; noisy GRF image; pavement crack assessment; pavement crack extraction; pavement images; road maintenance; road point-nonroad point separation; signal-to-noise ratio; vehicle trajectory data; Apertures; Data mining; Noise; Roads; Tensile stress; Three-dimensional displays; Vehicles; Georeferenced; ITVCrack; intensity; iterative tensor voting (ITV); mobile laser scanning (MLS); pavement crack extraction;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2014.2344714