  Vehicle detection is a significant and challenging task in aerial remote
sensing applications. Most existing methods detect vehicles with regular
rectangle boxes and fail to offer the orientation of vehicles. However, the
orientation information is crucial for several practical applications, such as
the trajectory and motion estimation of vehicles. In this paper, we propose a
novel deep network, called rotatable region-based residual network (R$^3$-Net),
to detect multi-oriented vehicles in aerial images and videos. More specially,
R$^3$-Net is utilized to generate rotatable rectangular target boxes in a half
coordinate system. First, we use a rotatable region proposal network (R-RPN) to
generate rotatable region of interests (R-RoIs) from feature maps produced by a
deep convolutional neural network. Here, a proposed batch averaging rotatable
anchor (BAR anchor) strategy is applied to initialize the shape of vehicle
candidates. Next, we propose a rotatable detection network (R-DN) for the final
classification and regression of the R-RoIs. In R-DN, a novel rotatable
position sensitive pooling (R-PS pooling) is designed to keep the position and
orientation information simultaneously while downsampling the feature maps of
R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our
network on two open vehicle detection image datasets, namely DLR 3K Munich
Dataset and VEDAI Dataset, demonstrating the high precision and robustness of
our method. In addition, further experiments on aerial videos show the good
generalization capability of the proposed method and its potential for vehicle
tracking in aerial videos. The demo video is available at
https://youtu.be/xCYD-tYudN0.
