Investigation on the Use of Polarized Images for Frozen Road Surface Recognition

In this study, we investigate the optimal capture conditions and features for recognizing frozen road surface conditions using polarization images. We focus on three types of road surfaces— asphalt, concrete, and metal—and classify them into dry, wet, and frozen states. Among the capture conditions, in particular, we examine how the positional relationship between the camera and the lighting influences the recognition of frozen road surface conditions. The results indicated that when the camera and lighting were aligned in the same direction, frozen conditions were successfully recognized on the asphalt surface. Conversely, when the camera and lighting were positioned in opposite directions, frozen conditions were observed on the concrete and metal surfaces. To evaluate the features used in image processing, we used a support vector machine to classify road surface images based on color and polarization information. The results demonstrate successful classification of images into frozen and wet/dry states. In this study, we clarified the appropriate capture conditions and features for frozen condition recognition based on the relationship between frozen road surface conditions and polarization characteristics, as well as the classification outcomes using these features.