Pavement distress detection and classification using feature mapping

Abstract

The detection of cracks and other degradations on pavement surfaces was traditionally done by human experts using visual inspection while driving along the surveyed road. To overcome the limitations of the manual scheme, an automatic crack detection and classification system is proposed in this paper to both speed up and reduce the subjectivity of the process. After the pavement images are captured by a digital camera, regions corresponding to cracks are detected over the acquired images by local segmentation and then represented by a matrix of square tiles. Since the crack pattern can be represented by the distribution of the crack tiles, standard deviations for both vertical and horizontal histograms are calculated to map the cracks onto a 2D feature space, where four crack types, namely, longitudinal, transversal, block, and alligator cracks can be identified. The experimental results, obtained by testing real pavement images over local asphalt roads, present the effectiveness of our algorithm for automating the process of identifying road distresses from images.