  On-board real-time vehicle detection is of great significance for UAVs and
other embedded mobile platforms. We propose a computationally inexpensive
detection network for vehicle detection in UAV imagery which we call
ShuffleDet. In order to enhance the speed-wise performance, we construct our
method primarily using channel shuffling and grouped convolutions. We apply
inception modules and deformable modules to consider the size and geometric
shape of the vehicles. ShuffleDet is evaluated on CARPK and PUCPR+ datasets and
compared against the state-of-the-art real-time object detection networks.
ShuffleDet achieves 3.8 GFLOPs while it provides competitive performance on
test sets of both datasets. We show that our algorithm achieves real-time
performance by running at the speed of 14 frames per second on NVIDIA Jetson
TX2 showing high potential for this method for real-time processing in UAVs.
