  Recently, object detection in aerial images has gained much attention in
computer vision. Different from objects in natural images, aerial objects are
often distributed with arbitrary orientation. Therefore, the detector requires
more parameters to encode the orientation information, which are often highly
redundant and inefficient. Moreover, as ordinary CNNs do not explicitly model
the orientation variation, large amounts of rotation augmented data is needed
to train an accurate object detector. In this paper, we propose a
Rotation-equivariant Detector (ReDet) to address these issues, which explicitly
encodes rotation equivariance and rotation invariance. More precisely, we
incorporate rotation-equivariant networks into the detector to extract
rotation-equivariant features, which can accurately predict the orientation and
lead to a huge reduction of model size. Based on the rotation-equivariant
features, we also present Rotation-invariant RoI Align (RiRoI Align), which
adaptively extracts rotation-invariant features from equivariant features
according to the orientation of RoI. Extensive experiments on several
challenging aerial image datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016, show that
our method can achieve state-of-the-art performance on the task of aerial
object detection. Compared with previous best results, our ReDet gains 1.2, 3.5
and 2.6 mAP on DOTA-v1.0, DOTA-v1.5 and HRSC2016 respectively while reducing
the number of parameters by 60\% (313 Mb vs. 121 Mb). The code is available at:
\url{https://github.com/csuhan/ReDet}.
