Pavement pathologies classification using graph-based features

Pavement cracks involve important information to measure road quality. Crack classification is a challenging problem given the diversity of possible cracks, therefore, it is needed to retrieve good features in order to facilitate the learning of predictive models with as few samples as possible. In this paper, we propose a graph-based set of features to efficiently describe cracks. These features proved to have high degree of expressiveness and robustness when used for crack classification. We show that the proposed features succeed in the assessment of 525 images with different kinds of cracks. We proved the robustness of the approach applying different levels of noise to the images and evaluating the classification accuracy.