M amba SOD : A Low‐Complexity Aerial Small Object Detection Framework With State Space Models and Deformable Feature Refinement

Object detection in high‐resolution aerial imagery has become a central topic in computer vision, yet it remains hindered by three challenges: (i) tiny, densely distributed targets are easily overwhelmed by cluttered backgrounds, leading to missed detections and inaccurate localization; (ii) existing methods inadequately refine shallow features with global semantics, making fine‐grained textures and boundary cues vulnerable to background interference; (iii) quadratic‐complexity feature interactions cause high computational overhead, limiting their practicality for high‐resolution aerial images. To address these problems, we propose MambaSOD, a small‐object‐oriented detection framework for aerial imaging that integrates deformable detail enhancement while achieving a favorable trade‐off between detection accuracy and computational cost. First, we propose SODMamba, an SS2D‐based state‐space backbone adapted for aerial small object detection, which strengthens global contextual modeling with linear computational complexity. Second, IDWTUP restores high‐frequency cues during upsampling to preserve tiny‐object details. Third, Enhance‐X performs scale‐aware deformable feature recalibration to refine shallow representations with contextual semantics. Extensive experiments on multiple aerial datasets demonstrate that MambaSOD achieves state‐of‐the‐art performance at less than half the computational cost of mainstream detectors. On the challenging VisDrone2021 dataset, it improves AP and AP 50 by 5.3% and 6.6%, respectively, demonstrating its superiority in small object detection.