MCI-DETR: A Multi-Level Context Interaction Transformer for Small-Object Detection in UAV Imagery

Small objects in UAV imagery often exhibit weak visual representations, suffer from background clutter, and receive insufficient cross-scale feature interaction. To address these challenges, we develop MCI-DETR, a multi-level contextual interaction detector built upon RT-DETR. First, a Local-Context Modulated Interaction Module (LCMI) is designed to selectively modulate contextual features using local structural responses, thereby suppressing irrelevant background responses while expanding the effective receptive field. Second, CATM is incorporated into the high-level feature interaction stage to form a Context Interaction Module (CCIM), strengthening target-related information through spatial- and channel-domain context mapping and additive Query–Key interaction and reducing the interference of complex backgrounds with the high-level semantic representation of weak small objects. Finally, a Multi-Scale Adaptive Gated Fusion Module (MSAGF) is designed to dynamically adjust the fusion contributions of information at different scales according to the input features, while improving the quality of cross-scale feature fusion through gated coordination between local details and global context. On VisDrone2019, MCI-DETR surpasses RT-DETR by 5.2%, 4.8%, and 3.2% in mAP50, mAP50-95, and APS, respectively, demonstrating enhanced robustness for small-object recognition in cluttered aerial imagery. MCI-DETR also achieves competitive performance on UAVDT and HIT-UAV against representative detectors, demonstrating the effectiveness of the proposed design under different UAV scenes and imaging conditions. Overall, MCI-DETR strengthens the representation of small objects in aerial scenes and maintains reliable detection performance in the presence of cluttered backgrounds.