  Drones have proven to be useful in many industry segments such as security
and surveillance, where e.g. on-board real-time object tracking is a necessity
for autonomous flying guards. Tracking and following suspicious objects is
therefore required in real-time on limited hardware. With an object detector in
the loop, low latency becomes extremely important. In this paper, we propose a
solution to make object detection for UAVs both fast and super accurate. We
propose a multi-dataset learning strategy yielding top eye-sky object detection
accuracy. Our model generalizes well on unseen data and can cope with different
flying heights, optically zoomed-in shots and different viewing angles. We
apply optimization steps such that we achieve minimal latency on embedded
on-board hardware by fusing layers, quantizing calculations to 16-bit floats
and 8-bit integers, with negligible loss in accuracy. We validate on NVIDIA's
Jetson TX2 and Jetson Xavier platforms where we achieve a speed-wise
performance boost of more than 10x.
