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JBE, vol. 23, no. 6, pp.760-767, November, 2018

DOI: https://doi.org/10.5909/JBE.2018.23.6.760

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

Namhyun Ahn and Suk-Ju Kang

C.A E-mail: sjkang@sogang.ac.kr

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.



Keyword: Image translation, Image enhancement, Deep learning, Cycle consistency, Generative adversarial network

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