  In this paper we propose a method for improving pedestrian detection in the
thermal domain using two stages: first, a generative data augmentation approach
is used, then a domain adaptation method using generated data adapts an RGB
pedestrian detector. Our model, based on the Least-Squares Generative
Adversarial Network, is trained to synthesize realistic thermal versions of
input RGB images which are then used to augment the limited amount of labeled
thermal pedestrian images available for training. We apply our generative data
augmentation strategy in order to adapt a pretrained YOLOv3 pedestrian detector
to detection in the thermal-only domain. Experimental results demonstrate the
effectiveness of our approach: using less than 50\% of available real thermal
training data, and relying on synthesized data generated by our model in the
domain adaptation phase, our detector achieves state-of-the-art results on the
KAIST Multispectral Pedestrian Detection Benchmark; even if more real thermal
data is available adding GAN generated images to the training data results in
improved performance, thus showing that these images act as an effective form
of data augmentation. To the best of our knowledge, our detector achieves the
best single-modality detection results on KAIST with respect to the
state-of-the-art.
