Detecting tiny objects plays a vital role in remote sensing intelligent interpretation, as these objects often carry critical information for downstream applications. However, due to the extremely limited pixel information and significant variations in object density, mainstream Transformer-based detectors often suffer from slow convergence and inaccurate query-object matching. To address these challenges, we propose D$^3$R-DETR, a novel DETR-based detector with Dual-Domain Density Refinement. By fusing spatial and frequency domain information, our method refines low-level feature maps and utilizes their rich details to predict more accurate object density map, thereby guiding the model to precisely localize tiny objects. Extensive experiments on the AI-TOD-v2 dataset demonstrate that D$^3$R-DETR outperforms existing state-of-the-art detectors for tiny object detection.

翻译：在遥感智能解译中，小目标检测至关重要，因为这些目标通常承载着下游应用的关键信息。然而，由于像素信息极其有限且目标密度差异显著，主流的基于Transformer的检测器往往存在收敛速度慢、查询-目标匹配不准确的问题。为应对这些挑战，本文提出D$^3$R-DETR，一种基于DETR的新型检测器，具备双域密度优化能力。该方法通过融合空间域与频域信息，优化低级特征图，并利用其丰富的细节预测更精确的目标密度图，从而引导模型准确定位小目标。在AI-TOD-v2数据集上的大量实验表明，D$^3$R-DETR在小目标检测任务上优于现有的先进检测器。