Hybrid-DETR: A Differentiated Module-Based Model for Object Detection in Remote Sensing Images

Currently, embedded unmanned aerial vehicle (UAV) systems face significant challenges in balancing detection accuracy and computational efficiency when processing remote sensing images with complex backgrounds, small objects, and occlusions. This paper proposes the Hybrid-DETR model based on a real-time end-to-end Detection Transformer (RT-DETR), featuring a novel HybridNet backbone network that implements a differentiated hybrid structure through lightweight RepConv Cross-stage Partial Efficient Layer Aggregation Network (RCSPELAN) modules and the Heat-Transfer Cross-stage Fusion (HTCF) modules, effectively balancing feature extraction efficiency and global perception capabilities. Additionally, we introduce a Small-Object Detection Module (SODM) and an EIFI module to enhance the detection capability of small objects in complex scenarios, while employing the Focaler-Shape-IoU loss function to optimize bounding box regression. Experimental results on the VisDrone2019 dataset demonstrate that Hybrid-DETR achieves mAP50 and mAP50:95 scores of 52.2% and 33.3%, respectively, representing improvements of 5.2% and 4.3% compared to RT-DETR-R18, while reducing model parameters by 29.33%. The effectiveness and robustness of our improved method are further validated on multiple challenging datasets, including AI-TOD and HIT-UAV.