Domain Adaptation from Visible-Light to FIR with Reliable Pseudo Labels

Deep learning object detection models using visible-light cameras are easily affected by weather and lighting conditions, whereas those using far-infrared cameras are less affected by such conditions. This paper proposes a domain adaptation method using pseudo labels from a visible-light camera toward an accurate object detection from far-infrared images. Our method projects visible light-domain detection results onto far-infrared images, and uses them as pseudo labels for training a far-infrared detection model. We confirmed the effectiveness of our method through experiments.