Performance Evaluation of Detection Model for Road Surface Damage using YOLO

The social infrastructure, including roads and bridges built during Japan's period of rapid economic growth, is rapidly deteriorating, and there is a need to strategically maintain and renew the social infrastructure that is aging all at once. On the other hand, in road maintenance and management in rural areas, it is not realistic to increase the number of road management patrol cars or the number of specialized engineers engaged in road maintenance and management, and the reduction of management budgets and the shortage of engineers due to the declining birthrate and aging population are serious problems. In addition, in rural areas, it is difficult to conduct all road inspections by visual inspection, which is performed by expert road maintenance technicians, and an inexpensive, high-precision system that can automatically detect road surface damage through image analysis or other means is required. In this study, we construct a road surface damage detection model using YOLOv5, a machine learning algorithm capable of real-time.