DyPerceiver-Det: Instance-Wise Dynamic Perception for Fine-Grained Oriented Object Detection in Remote Sensing Images

Fine-grained oriented object detection in remote sensing is challenged by extreme scale variation, arbitrary rotations, and dense layouts with structured clutter, where fixed feature selection and RoI extraction often lead to unstable localization and intra-class confusion. We propose DyPerceiver-Det, a plug-and-play Dynamic Perception RoI Extractor for RoI-based oriented detectors. Given multi-level feature maps and oriented proposals, DyPerceiver-Det refines each RoI representation along three complementary dimensions: Dynamic Scale Perception (DSP) for instance-wise multi-level feature fusion, Dynamic Channel Perception (DCP) for RoI-specific channel gating to suppress background-dominant responses, and Dynamic Orientation Perception (DOP) for orientation-aligned local sampling with learnable refinement. We further introduce sparsity and consistency regularization to make dynamic decisions selective and stable under strong geometric augmentations. Extensive experiments on FAIR1M and MAR20 under a unified evaluation protocol demonstrate consistent improvements over strong oriented baselines, with more pronounced gains on small objects and crowded regions. Qualitative visualizations and fine-grained confusion analyses provide interpretable evidence that dynamic perception reduces feature misalignment and sibling-class confusions with modest computational overhead.