Weather Data Integrated Mask R-CNN for Automatic Road Surface Condition Monitoring

Abstract

Monitoring road surface conditions plays a crucial role in driving safety and road maintenance, especially in winter seasons. Traditional methodologies often employ manual inspection and expensive instruments, e.g., NIR cameras. However, image analysis based on normal cameras can provide an economical and efficient solution for road surface monitoring. This paper presents an automatic classification model of road surface conditions using a deep learning approach based on road images and weather measurement. A modified mask R-CNN model has been developed by integrating weather data based on transfer learning. Experimental results with respect to manual judgment of road surface conditions have demonstrated very high accuracy of the developed model.