MAR-YOLO: multi-scale feature adaptive selection and asymptotic pyramid for oriented Building detection in remote sensing images

Building detection in remote sensing imagery confronts three interdependent challenges including extreme scale variance under dense spatial distributions, orientation instability in off-nadir imagery, and semantic gaps during multi-scale feature fusion. Existing methods address these challenges in isolation, resulting in performance degradation when challenges co-occur. MAR-YOLO establishes an integrated rotated object detection framework extending YOLOv11-OBB through three synergistic innovations. The Multi-scale Feature Adaptive Selection (MFAS) module adaptively filters P2-P5 features through dual- domain weighting, enhancing small building perception while suppressing redundancy. The adapted Adaptive Feature Pyramid Network (AFPN) employs progressive fusion with scale-matched kernels and learned spatial weights, eliminating semantic inconsistencies inherent in direct multi-scale concatenation. The RepVGG-based Enhanced Rotated Detection Head (RRD-Head) applies branch-specialized structural reparameterization addressing angle regression instability. Validation on BONAI demonstrates 87.2% mAP50 and 65.3% mAP50-95, representing 2.9% and 2.6% improvements over YOLOv11s-OBB at 95 FPS. Cross-dataset experiments on DOTA, DIOR-R, and HRSC2016 confirm architectural robustness across diverse detection scenarios.