Snowy Night-to-Day Translator and Semantic Segmentation Label Similarity for Snow Hazard Indicator
Takato Yasuno, Hiroaki Sugawara, Junichiro Fujii, Ryuto Yoshida

TL;DR
This paper introduces an automated snow hazard indicator system using conditional GANs and semantic segmentation to assess snow coverage on roads at night, aiding in road safety management during winter snow events.
Contribution
It presents a novel combination of image translation and semantic segmentation techniques to evaluate snow coverage on roads from night images, improving hazard detection accuracy.
Findings
High similarity between generated night-to-day images and real snowy day images.
Effective detection of snow-covered road regions at night.
Potential to assist road management in snow hazard situations.
Abstract
In 2021, Japan recorded more than three times as much snowfall as usual, so road user maybe come across dangerous situation. The poor visibility caused by snow triggers traffic accidents. For example, 2021 January 19, due to the dry snow and the strong wind speed of 27 m / s, blizzards occurred and the outlook has been ineffective. Because of the whiteout phenomenon, multiple accidents with 17 casualties occurred, and 134 vehicles were stacked up for 10 hours over 1 km. At the night time zone, the temperature drops and the road surface tends to freeze. CCTV images on the road surface have the advantage that we enable to monitor the status of major points at the same time. Road managers are required to make decisions on road closures and snow removal work owing to the road surface conditions even at night. In parallel, they would provide road users to alert for hazardous road surfaces.…
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Taxonomy
TopicsFire Detection and Safety Systems · Cryospheric studies and observations · Fire effects on ecosystems
