  Object detection in aerial images is an important task in environmental,
economic, and infrastructure-related tasks. One of the most prominent
applications is the detection of vehicles, for which deep learning approaches
are increasingly used. A major challenge in such approaches is the limited
amount of data that arises, for example, when more specialized and rarer
vehicles such as agricultural machinery or construction vehicles are to be
detected. This lack of data contrasts with the enormous data hunger of deep
learning methods in general and object recognition in particular. In this
article, we address this issue in the context of the detection of road vehicles
in aerial images. To overcome the lack of annotated data, we propose a
generative approach that generates top-down images by overlaying artificial
vehicles created from 2D CAD drawings on artificial or real backgrounds. Our
experiments with a modified RetinaNet object detection network show that adding
these images to small real-world datasets significantly improves detection
performance. In cases of very limited or even no real-world images, we observe
an improvement in average precision of up to 0.70 points. We address the
remaining performance gap to real-world datasets by analyzing the effect of the
image composition of background and objects and give insights into the
importance of background.
