Learning Symmetry-Aware RoI Representations for Oriented Object Detection

Oriented detection localizes objects with boxes that include orientation, and has found broad applications in embodied intelligence, autonomous driving, and satellite remote sensing. Two-stage oriented detectors typically extract features from rotated proposals, then use these features for classification and box refinement in each proposal's local coordinate frame. However, their detection heads typically do not explicitly account for the symmetry of the proposal's local coordinate frame. Reversing either local axis gives an equivalent description of the same proposal, but affects the prediction targets differently. Class and size remain unchanged, while center offsets and relative angles undergo predictable sign changes. We argue that region features should follow the same transformation laws as the targets they predict. To this end, we propose Symmetry-Aware Region Representations (SRR). We extract features in four equivalent local frames and combine them through the proposed Geometry-Consistent Aggregation (GCA), which weights the frames according to how each target changes sign. We further propose Symmetry-Aware Feature Learning (SFL), which divides features into types by their transformation laws and learns a separate linear map for each type, keeping the head equivariant to local axis reversals. Experiments on DOTA-v1.0, HRSC2016, DIOR-R, RSAR, and FAIR1M show that SRR improves detection performance, with localization gains across multiple detectors and backbones on DOTA-v1.0.