Lightweight road manager: smartphone-based automatic determination of road damage status by deep neural network

Abstract

Citizens in various locations can report local infrastructure issues to the government by posting reports on certain websites, such as Chiba Report and FixMyStreet. Recently, these systems have begun operating worldwide. In these systems, a large volume of information is collected on infrastructure problems that are identified by citizens (e.g., broken paving slabs, fly tipping, graffiti, potholes). This information is expected to be utilized for infrastructure maintenance. However, local problems (especially road defection) identified by citizens are sometimes not deemed an urgent matter for road managers. This is because it is difficult for an average person to determine road damage status. Furthermore, non-critical reports may be a burden for local government because each report requires visual confirmation. We therefore propose a smartphone application based on a deep neural network that can determine road damage status using only photographs of the road. This application is based on a deep neural network model trained by citizen reports and road manager inspection results, which are gathered daily on a government server. The application updates the model parameters each time it launches and thereby becomes increasingly more intelligent and effective. The proposed system enables average citizens to easily determine road damage status using only a smartphone application. In addition, because not only expert road managers but also local government officials without expert knowledge can inspect the road, the proposed system can be useful for local governments that lack expert road managers.