  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 large 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. 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 datasets across multiple models and metrics. In particular, we achieve
a new state-of-the-art performance on UAVDT for real-time detectors.
Furthermore, we create a new airborne image dataset by annotating 13 713
objects in 2 900 images featuring precise altitude and viewing angle
annotations.
