PSG-RTDETR: Towards Stable Cross-Scale Feature Fusion for Small Object Detection

Small object detection in UAV remote sensing remains challenging due to fine-grained feature loss during downsampling and unbalanced cross-scale feature aggregation. Conventional feature pyramid fusion may suffer from hard activation truncation or competitive weight suppression when heterogeneous feature scales are fused. To address these issues, we propose PSG-RTDETR, a P2-aware Softplus-Gated RT-DETR framework tailored for UAV-based dense small-object detection. Specifically, we integrate high-resolution P2 features into a bidirectional feature pyramid to preserve critical spatial details. Furthermore, we introduce Softplus normalization for smooth weight optimization and a sample-adaptive gating mechanism to dynamically suppress noisy shallow responses. Extensive experiments on VisDrone-DET demonstrate that, compared with the RT-DETR-ResNet18 baseline under the same ablation setting, PSG-RTDETR improves mAP50–95 and APsmall by 4.1 and 4.7 percentage points, respectively. Further validation on HIT-UAV confirms its strong generalizability across infrared modalities. These accuracy gains involve a clear computational trade-off: GFLOPs increase from 57.20 to approximately 110.23, while FPS decreases from 126.18 to 80.02 on an RTX 3090. Thus, PSG-RTDETR is better suited to accuracy-oriented off-board or near-real-time GPU analysis than to direct deployment on resource-constrained onboard UAV devices without further compression or acceleration.