DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

Xinyi Wu

Abstract:

Semantic segmentation of nighttime images plays an
equally important role as that of daytime images in autonomous driving, but the former is much more challenging
due to poor illuminations and arduous human annotations.
In this paper, we propose a novel domain adaptation network (DANNet) for nighttime semantic segmentation without using labeled nighttime image data. It employs an adversarial training with a labeled daytime dataset and an
unlabeled dataset that contains coarsely aligned day-night
image pairs. Specifically, for the unlabeled day-night image pairs, we use the pixel-level predictions of static object
categories on a daytime image as a pseudo supervision to
segment its counterpart nighttime image. We further design
a re-weighting strategy to handle the inaccuracy caused
by misalignment between day-night image pairs and wrong
predictions of daytime images, as well as boost the prediction accuracy of small objects. The proposed DANNet is the
first one-stage adaptation framework for nighttime semantic segmentation, which does not train additional day-night
image transfer models as a separate pre-processing stage.
Extensive experiments on Dark Zurich and Nighttime Driving datasets show that our method achieves state-of-the-art
performance for nighttime semantic segmentation.