Abstract
Black ice is a perilous phenomenon resulting from frozen precipitation on road surfaces. It stands as one of the most prominent contributors to winter traffic accidents since it is nearly invisible. Given these characteristics, there is a pressing need to develop automotive systems capable of detecting black ice to improve driving safety and conditions. This study proposes the deployment of two YOLOX-based models to implement real-time black ice detection from drone-view images. The proposed approach leverages YOLOX-Tiny, known for its proficiency in real-time object detection, in conjunction with YOLOX-B which is a refined version designed specifically for detecting black ice on roadways. For training and evaluating the proposed system, we collected a dataset of black ice consisting of 2,851 sample images sized at 416x416 pixels. Significantly, YOLOX-Tiny achieved an AP@[0.5:0.95] of 0.4923, whereas YOLOX-B achieved 0.4779. Additionally, we demonstrate a practical implementation of the proposed method by deploying the system on the Nvidia Jetson Orin Nano device for real-time inference. The black ice dataset we compiled is publicly available on GitHub. We expect that the proposed black ice detection system will contribute to effectively maintaining safer driving conditions.
Published Version
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