Road managers conduct visual inspections and road condition surveys to identify road pavement damage, but there is a shortage of manpower and cost issues. To solve this problem, a method for detecting rut excavation by applying deep learning image analysis to video images captured by an in-vehicle camera has attracted attention. Existing studies have demonstrated methods using single-lens reflex cameras and video cameras, etc. However, detection using easily available video images from drive recorders and smartphones is expected to accelerate the development of infrastructure DX.

 In this study, we developed a method for detecting rut excavation using image segmentation based on deep learning by correcting the gamma value representing the luminance of 4K video images captured with a drive recorder and a smartphone. As a result, Mask R-CNN and YOLOv8 were able to construct models with fewer false positives and omissions, respectively, suggesting useful implications for practical applications.