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http://dx.doi.org/10.5909/JBE.2018.23.6.760

Night-to-Day Road Image Translation with Generative Adversarial Network for Driver Safety Enhancement  

Ahn, Namhyun (Dept. of Electronic Engineering, Sogang University)
Kang, Suk-Ju (Dept. of Electronic Engineering, Sogang University)
Publication Information
Journal of Broadcast Engineering / v.23, no.6, 2018 , pp. 760-767 More about this Journal
Abstract
Advanced driver assistance system(ADAS) is a major technique in the intelligent vehicle field. The techniques for ADAS can be separated in two classes, i.e., methods that directly control the movement of vehicle and that indirectly provide convenience to driver. In this paper, we propose a novel system that gives a visual assistance to driver by translating a night road image to a day road image. We use the black box images capturing the front road view of vehicle as inputs. The black box images are cropped into three parts and simultaneously translated into day images by the proposed image translation module. Then, the translated images are recollected to original size. The experimental result shows that the proposed method generates realistic images and outperforms the conventional algorithms.
Keywords
Image translation; Image enhancement; Deep learning; Cycle consistency; Generative adversarial network;
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