  Despite much research progress in image semantic segmentation, it remains
challenging under adverse environmental conditions caused by imaging
limitations of visible spectrum. While thermal infrared cameras have several
advantages over cameras for the visible spectrum, such as operating in total
darkness, insensitive to illumination variations, robust to shadow effects and
strong ability to penetrate haze and smog. These advantages of thermal infrared
cameras make the segmentation of semantic objects in day and night. In this
paper, we propose a novel network architecture, called edge-conditioned
convolutional neural network (EC-CNN), for thermal image semantic segmentation.
Particularly, we elaborately design a gated feature-wise transform layer in
EC-CNN to adaptively incorporate edge prior knowledge. The whole EC-CNN is
end-to-end trained, and can generate high-quality segmentation results with the
edge guidance. Meanwhile, we also introduce a new benchmark dataset named
"Segment Objects in Day And night"(SODA) for comprehensive evaluations in
thermal image semantic segmentation. SODA contains over 7,168 manually
annotated and synthetically generated thermal images with 20 semantic region
labels and from a broad range of viewpoints and scene complexities. Extensive
experiments on SODA demonstrate the effectiveness of the proposed EC-CNN
against the state-of-the-art methods.
