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An Easy Infrastructure Management Method Using On-Board Smartphone Images and Citizen Reports by Deep Neural Network

Lack of funding and human resources in small municipalities is a serious issue in Japanese infrastructure maintenance today. Meanwhile, systems in which citizens report local issues to the government by posting them on websites such as "Chiba-Repo" and "FixMyStreet" have started to operate worldwide. In these systems, a large volume of information on problems with infrastructure (e.g., broken paving slabs, fly tipping, graffiti, potholes) found by citizens is collected. The information collected by citizens is expected to be utilized for infrastructure maintenance. However, information gathered by citizens has deviations in the spatial dimension and in its content. This research proposes to complement deviating but voluminous information provided by citizens by combining smartphone videos of roads. In order to identify and establish the level of defects on roads from photos taken by smartphones, this research uses a photo processing method that utilizes deep learning to create a model that automatically identifies road defects. As a result, the model is able to spot road damage with an accuracy of more than 93%. In addition, this model shows the possibility of categorizing voluminous photo data provided by citizens.