  Object detection from Unmanned Aerial Vehicles (UAVs) is of great importance
in many aerial vision-based applications. Despite the great success of generic
object detection methods, a significant performance drop is observed when
applied to images captured by UAVs. This is due to large variations in imaging
conditions, such as varying altitudes, dynamically changing viewing angles, and
different capture times. These variations lead to domain imbalances and, thus,
trained models suffering from domain bias. We demonstrate that domain knowledge
is a valuable source of information and thus propose domain-aware object
detectors by using freely accessible sensor data. By splitting the model into
cross-domain and domain-specific parts, substantial performance improvements
are achieved on multiple data sets across various models and metrics without
changing the architecture. In particular, we achieve a new state-of-the-art
performance on UAVDT for embedded real-time detectors. Furthermore, we create a
new airborne image data set by annotating 13,713 objects in 2,900 images
featuring precise altitude and viewing angle annotations.
