基于张量投票的道路表面裂缝检测

doi:10.3969/j.issn.0255-8297.2015.05.008

Abstract

Abstract: This paper proposes a multi-scale tensor voting framework that applies tensor voting to mobile laser scanning data to extract pavement cracks. Trajectory data are used to extract road curbs from profiles along the travelling line to separate road points from non-road points. The extracted road points are interpolated into road feature images. Thus curvilinear cracks are enhanced and extracted with a multi-scale tensor voting framework. Experiments on mobile laser scanning data and road image data were carried out. The results show that the method is robust to noise in both road images and feature images, and can achieve good performance in pavement crack extraction.

Key words: tensor voting, mobile LiDAR data, pavement cracks, road feature image

CLC Number:

P237

LI Ai-xia, GUAN Hai-yan, ZHONG Liang, YU Yong-tao. Tensor Voting Based Pavement Crack Extraction[J]. Journal of Applied Sciences, 2015, 33(5): 541-549.

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Tensor Voting Based Pavement Crack Extraction

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