Object-Based Crack Detection and Attribute Extraction From Laser-Scanning 3D Profile Data

Cracks in 3D pavement data often show poor continuity, low contrast and different depths, which bring great challenges to related application. Recently, crack attributes, e.g. depth and width have attracted attention of highway agencies for maintenance decision-makings, but few studies have been conducted on crack attributes. This paper presents object-based image analysis (OBIA) method for crack detection and attribute extraction from laser-scanning 3D profile data with elevation accuracy about 0.25 mm. Firstly, a high-pass filter designed for pavement components in our previous research was applied to remove the fluctuation posture in 3D data, and then the smallest of-constant false-alarm rate algorithm was used to acquire lower point sets, including crack seeds and lower textures. Secondly, the objects were represented by above obtained 3D point sets and OBIA, especially, the depth statistics, shape and topological features of objects were described. Moreover, to enhance crack objects and remove texture objects gradually, multi-scale object selections and merges were conducted according to the local statistical characteristics differences of objects. Thirdly, the objects' orientation attributes were combined with tensor voting to connect and infer final crack objects, and then the object-level crack depth attributes could be extracted. The experimental results demonstrated that proposed method achieved average buffered Hausdorff scores of 94.39, Recall of 0.92 and F -value of 0.91 for crack detection on 30 real measured 3D asphalt pavement data. Furthermore, crack depth attributes can be extracted at different scales according requirements, the obtained location and depth attributes provide more comprehensive information for pavement maintenances.