A Novel Path Voting Algorithm for Surface Crack Detection

Abstract

Path voting is a widely used technique for line structure detection in images. Traditional path voting, based on minimal-path, is performed to track paths based on how seeds grow. The former requires to set a starting point and an end point. Thus, the performance of minimal-path path voting depends on the initialization. However, high-quality initialization often requires human interaction, which limits its applications in practice. In this paper, a fully automatic path voting method has been proposed and applied for crack detection. The proposed path voting is performed to segment images, which partitions an image patch along the potential crack path and integrates the path to form a crack probability map. After path voting, crack seeds are sampled and modeled into a graph, and the edge weights are assigned using an attraction field algorithm. Finally, cracks are extracted by using spanning tree and tree pruning algorithms. Experimental results demonstrate that the proposed path voting approach can effectively infer the cracks from 2D optic images and 3D depth images.