%0 Journal Article 
%A Romera Carmena, Eduardo
%A Bergasa Pascual, Luis Miguel
%A Yang, Kailun
%A Alvarez, Jose M.
%A Barea Navarro, Rafael
%T Bridging the day and night domain gap for semantic segmentation
%D 2019
%U http://hdl.handle.net/10017/45110
%X Perception in autonomous vehicles has progressed
exponentially in the last years thanks to the advances of visionbased methods such as Convolutional Neural Networks (CNNs).
Current deep networks are both efficient and reliable, at least
in standard conditions, standing as a suitable solution for the
perception tasks of autonomous vehicles. However, there is
a large accuracy downgrade when these methods are taken
to adverse conditions such as nighttime. In this paper, we
study methods to alleviate this accuracy gap by using recent
techniques such as Generative Adversarial Networks (GANs).
We explore diverse options such as enlarging the dataset to
cover these domains in unsupervised training or adapting the
images on-the-fly during inference to a comfortable domain
such as sunny daylight in a pre-processing step. The results
show some interesting insights and demonstrate that both
proposed approaches considerably reduce the domain gap,
allowing IV perception systems to work reliably also at night.
%K Electrónica
%K Electronics
%~ Biblioteca Universidad de Alcala