  Rotation-equivariance is an essential yet challenging property in oriented
object detection. While general object detectors naturally leverage robustness
to spatial shifts due to the translation-equivariance of the conventional CNNs,
achieving rotation-equivariance remains an elusive goal. Current detectors
deploy various alignment techniques to derive rotation-invariant features, but
still rely on high capacity models and heavy data augmentation with all
possible rotations. In this paper, we introduce a Fully Rotation-Equivariant
Oriented Object Detector (FRED), whose entire process from the image to the
bounding box prediction is strictly equivariant. Specifically, we decouple the
invariant task (object classification) and the equivariant task (object
localization) to achieve end-to-end equivariance. We represent the bounding box
as a set of rotation-equivariant vectors to implement rotation-equivariant
localization. Moreover, we utilized these rotation-equivariant vectors as
offsets in the deformable convolution, thereby enhancing the existing
advantages of spatial adaptation. Leveraging full rotation-equivariance, our
FRED demonstrates higher robustness to image-level rotation compared to
existing methods. Furthermore, we show that FRED is one step closer to non-axis
aligned learning through our experiments. Compared to state-of-the-art methods,
our proposed method delivers comparable performance on DOTA-v1.0 and
outperforms by 1.5 mAP on DOTA-v1.5, all while significantly reducing the model
parameters to 16%.
