Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data

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.