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Extracting Accessibility Attributes from Open Barrier-free Toilet Images using Object Detection

Accessibility information on public toilets is being released as open data by governments in many countries, but such data often fail to meet the needs of people who use a wheelchair. The goal of our project is to propose a service that efficiently collects and provides rich accessibility information needed by actual wheelchair users. To realize it, in this paper, we examine the feasibility of a method for extracting accessibility information from publicly available facility images provided by Tokyo Metropolitan Government as open data using object detection. The results of this experiment showed that the object detection accuracy of different types of equipment in barrier-free toilets such as handrails and ostomate equipment exceeded 90%, which indicates a potential of our approach to automatically extract rich accessibility information from open barrier-free toilet images.