  Infrared (IR) images are essential to improve the visibility of dark or
camouflaged objects. Object recognition and segmentation based on a neural
network using IR images provide more accuracy and insight than color visible
images. But the bottleneck is the amount of relevant IR images for training. It
is difficult to collect real-world IR images for special purposes, including
space exploration, military and fire-fighting applications. To solve this
problem, we created color visible and IR images using a Unity-based 3D game
editor. These synthetically generated color visible and IR images were used to
train cycle consistent adversarial networks (CycleGAN) to convert visible
images to IR images. CycleGAN has the advantage that it does not require
precisely matching visible and IR pairs for transformation training. In this
study, we discovered that additional synthetic data can help improve CycleGAN
performance. Neural network training using real data (N = 20) performed more
accurate transformations than training using real (N = 10) and synthetic (N =
10) data combinations. The result indicates that the synthetic data cannot
exceed the quality of the real data. Neural network training using real (N =
10) and synthetic (N = 100) data combinations showed almost the same
performance as training using real data (N = 20). At least 10 times more
synthetic data than real data is required to achieve the same performance. In
summary, CycleGAN is used with synthetic data to improve the IR image
conversion performance of visible images.
