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.

本文提出了一种名为 ShuffleDet 的计算效率高、适用于 UAV 图像的车辆检测网络，通过使用通道重组和分组卷积等方法实现了提高速度性能的目的。该算法在 CARPK 和 PUCPR + 数据集上的表现也显示了其具有竞争性的性能，能够达到 14fps 的实时处理速度，展现其在 UAV 实时处理领域的高潜力。