BASFR-Net: Learning Stagewise Residual Interactions for RGB-Infrared Oriented Object Detection

RGB-infrared oriented object detection in aerial remote sensing must exploit complementary appearance and thermal cues while preserving the spatial detail of small, arbitrarily oriented objects. Existing multimodal object detection methods often optimize a particular cross-modal fusion mechanism, although the role of interaction changes across feature extraction, multiscale aggregation, and prediction. We present BASFR-Net, a compact detector that formulates cross-modal fusion as stagewise residual interaction. It preserves base representations while progressively calibrating modality responses, reconciling semantic and spatial information, and routing local evidence to prediction. BASFR-Net achieves 84.30% and 76.80% [email protected] on DroneVehicle and VEDAI, outperforming a feature-concatenation baseline by 2.00 and 7.60 points, respectively. On DroneVehicle, it reaches 69.64% [email protected]:0.95 with 5.06M parameters and 13.38 GFLOPs. Ablations show consistent gains from each interaction stage. These results establish stagewise residual interaction as an effective and compact design principle for RGB-infrared oriented object detection.