Augmented Aerial Reality: On Fusing Synthetic and Real Airborne Imagery for Object Detection

Object detection is a core task for image analysis and inter-pretation and is broadly applied in applications relying on space-and airborne imagery. Like all supervised deep learning methods, training an object detector generally requires a large amount of representative annotated data, which can be hard to acquire in practice. To overcome this challenge, generating synthetic data can be an option to alleviate a lack of real-world annotated data. One key influential factor for the quality of the synthetic data is the background. We show that the detectors' classifier especially depends severely on the background and has a large impact on the detection preci-sion. Using real background is a natural option, however, we show that this naive approach has drawbacks such as a sig-nificant drop in recall. In this paper, we demonstrate that by using style transfer to match the synthetic foreground to the real background, the detector can mitigate these drawbacks and achieve a more balanced result in terms of precision and recall.