WINTER ROAD SURFACE CONDITION CLASSIFICATION USING DEEP LEARNING WITH FOCAL LOSS BASED ON TEXT AND IMAGE INFORMATION

Abstract

This paper presents a winter road surface condition classification method using deep learning with focal loss based on text and image information for detecting the deterioration of road surface conditions caused by snow accumulation. The proposed method achieves multimodal road surface condition classification by constructing a deep learning model that can cooperatively use images automatically captured by fixed-point cameras installed along the road surface, and text data related to road surface conditions. Since the distribution of training data is biased toward winter road surface conditions, there is a concern that the classification accuracy may be degraded due to the data imbalance problem. Therefore, the proposed method uses focal loss, which can deal with data imbalance, to train a deep learning model to realize road surface condition classification considering data imbalance. In the end of this paper, we demonstrate the effectiveness of the proposed method by conducting experiments using real data.