[2N3-IS-2b] Machine learning (2/5)

[2N3-IS-2b-02] Snowy Night-to-Day Translator and Semantic Segmentation Label Similarity for Snow Hazard Indicator

〇Takato Yasuno Yasuno¹, Hiroaki Sugawara¹, Junichiro Fujii¹, Ryuto Yoshida¹ (1. Yachiyo Engineering Co.,Ltd. RIIPS)

Keywords:Winter Road Safety, Night CCTV, Conditional GAN , DeepLabv3+MobileNet , Snow Hazard Indicator

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. 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. This paper propose a method to automate a snow hazard indicator that the road surface region is generated from the night snow image using the Conditional GAN, pix2pix. In addition, the road surface and the snow covered ROI are predicted using the semantic segmentation DeepLabv3+ with a backbone MobileNet, and the snow hazard indicator to automatically compute how much the night road surface is covered with snow. We demonstrate several results applied to the cold and snow region in the winter of Japan January 19 to 21 2021, and mention the usefulness of high similarity between snowy night-to-day fake output and real snowy day image for night snow visibility.