  Currently, semantic segmentation shows remarkable efficiency and reliability
in standard scenarios such as daytime scenes with favorable illumination
conditions. However, in face of adverse conditions such as the nighttime,
semantic segmentation loses its accuracy significantly. One of the main causes
of the problem is the lack of sufficient annotated segmentation datasets of
nighttime scenes. In this paper, we propose a framework to alleviate the
accuracy decline when semantic segmentation is taken to adverse conditions by
using Generative Adversarial Networks (GANs). To bridge the daytime and
nighttime image domains, we made key observation that compared to datasets in
adverse conditions, there are considerable amount of segmentation datasets in
standard conditions such as BDD and our collected ZJU datasets. Our GAN-based
nighttime semantic segmentation framework includes two methods. In the first
method, GANs were used to translate nighttime images to the daytime, thus
semantic segmentation can be performed using robust models already trained on
daytime datasets. In another method, we use GANs to translate different ratio
of daytime images in the dataset to the nighttime but still with their labels.
In this sense, synthetic nighttime segmentation datasets can be generated to
yield models prepared to operate at nighttime conditions robustly. In our
experiment, the later method significantly boosts the performance at the
nighttime evidenced by quantitative results using Intersection over Union (IoU)
and Pixel Accuracy (Acc). We show that the performance varies with respect to
the proportion of synthetic nighttime images in the dataset, where the sweet
spot corresponds to most robust performance across the day and night.
