The majority of learning-based semantic segmentation methods are optimized for daytime
scenarios and favorable lighting conditions. Real-world driving scenarios, however, entail …

Current state-of-the-art image segmentation methods are mainly based on visible spectrum
images. However, it remains challenging under adverse environmental conditions (eg …

Most prior semantic segmentation methods have been developed for day-time scenes, while
typically underperforming in night-time scenes due to insufficient and complicated lighting …

Scene Parsing is an important cog for modern autonomous driving systems. Most of the
works in semantic segmentation pertains to day-time scenes with favourable weather and …

In recent years, considerable progress has been made in semantic segmentation of images
with favorable environments. However, the environmental perception of autonomous driving …

In recent years, intelligent driving navigation and security monitoring have made
considerable progress with the help of deep Convolutional Neural Networks (CNNs). As one …

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 …

The semantic segmentation of nighttime scenes is a challenging problem that is key to
impactful applications like self-driving cars. Yet, it has received little attention compared to its …

Semantic segmentation for autonomous driving should be robust against various in-the-wild
environments. Nighttime semantic segmentation is especially challenging due to a lack of …

Perception in autonomous vehicles has progressed exponentially in the last years thanks to
the advances of vision-based methods such as Convolutional Neural Networks (CNNs) …