3D Shadow Modeling for Detection of Descended Patterns on 3D Pavement Surface

Abstract

This paper proposes a novel algorithm called three-dimensional (3D) shadow modeling for the detection of various descended patterns on 3D pavement surfaces to improve detection accuracy; these patterns include pavement cracks, potholes, joints, and grooves. Analogous to a filtering algorithm that only preserves the useful signals, the proposed 3D shadow modeling is a general-purpose algorithm that can transform the raw 3D pavement data into a binary domain in which the amount of unneeded data, such as texture variations and noises in the original 3D data, can be reduced effectively and the remaining descended patterns then become distinctive. Bidirectional lighting is the essential concept specifically used in the 3D shadow modeling to find shadowed areas that are lower than the local surroundings. With the projection angle changes from 0 to 90°, the proposed 3D shadow modeling can generate diverse solutions from being extremely sensitive to extremely insensitive. In other words, the proposed 3D shadow modeling can satisfy varying needs on the basis of sensitivity. In complement to the detection of various descended patterns on pavement surface, the proposed 3D shadow modeling can be employed to detect ascended patterns once the original 3D pavement surface is vertically inverted. The experimental results demonstrated that the proposed 3D shadow modeling is an efficient algorithm and can yield a high level of precision (92.37%) and recall (92.93%) for the detection of typical descended patterns (potholes, cracks, joints, and grooves) on all selected 3D pavement images.