A method for detecting unnormal road surfaces using classifier trained only on image patches of normal road surface

Abstract

Local governments inspect road surfaces to detect deformations in their early stages. Since current road surface inspection methods are costly and time-consuming, image analysis using machine learning has been studied as a way to reduce cost and time. However, this method has difficulty detecting new types of deformations that are not included in the training data. In this paper, we propose a method to detect unnormal road surfaces using only image patches of normal road surfaces, where normal is defined asa road surface with no deformation. The proposed method is expected to enable efficient inspections by focusing on areas that are detected as unnormal. In the experimental section, the effectiveness of the proposed method is demonstrated using road surface images from Japan and Nigeria.