Ecology

Biology

Sunrise

Computer science

Dusk

Geology

Image segmentation

Physics

Sunset

Segmentation

Optics

Astronomy

Geography

Artificial intelligence

Daytime

Meteorology

Atmospheric sciences

Twilight

Adaptation (eye)

Computer vision

This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time - the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for knowledge transfer from daytime scenes to nighttime scenes, without human annotation.

Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime