  Object detection using aerial drone imagery has received a great deal of
attention in recent years. While visible light images are adequate for
detecting objects in most scenarios, thermal cameras can extend the
capabilities of object detection to night-time or occluded objects. As such,
RGB and Infrared (IR) fusion methods for object detection are useful and
important. One of the biggest challenges in applying deep learning methods to
RGB/IR object detection is the lack of available training data for drone IR
imagery, especially at night. In this paper, we develop several strategies for
creating synthetic IR images using the AIRSim simulation engine and CycleGAN.
Furthermore, we utilize an illumination-aware fusion framework to fuse RGB and
IR images for object detection on the ground. We characterize and test our
methods for both simulated and actual data. Our solution is implemented on an
NVIDIA Jetson Xavier running on an actual drone, requiring about 28
milliseconds of processing per RGB/IR image pair.
