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In 2021, Japan recorded more than three times as much snowfall as usual, soroad user maybe come across dangerous situation. The poor visibility caused bysnow triggers traffic accidents. For example, 2021 January 19, due to the drysnow and the strong wind speed of 27 m / s, blizzards occurred and the outlookhas been ineffective. Because of the whiteout phenomenon, multiple accidentswith 17 casualties occurred, and 134 vehicles were stacked up for 10 hours over1 km. At the night time zone, the temperature drops and the road surface tendsto freeze. CCTV images on the road surface have the advantage that we enable tomonitor the status of major points at the same time. Road managers are requiredto make decisions on road closures and snow removal work owing to the roadsurface conditions even at night. In parallel, they would provide road users toalert for hazardous road surfaces. This paper propose a method to automate asnow hazard indicator that the road surface region is generated from the nightsnow image using the Conditional GAN, pix2pix. In addition, the road surfaceand the snow covered ROI are predicted using the semantic segmentationDeepLabv3+ with a backbone MobileNet, and the snow hazard indicator toautomatically compute how much the night road surface is covered with snow. Wedemonstrate several results applied to the cold and snow region in the winterof Japan January 19 to 21 2021, and mention the usefulness of high similaritybetween snowy night-to-day fake output and real snowy day image for night snowvisibility.

Snowy Night-to-Day Translator and Semantic Segmentation Label Similarity for Snow Hazard Indicator