通过双域密度精修，提升无人机图像中微小目标检测精度
D$^3$R-DETR: DETR with Dual-Domain Density Refinement for Tiny Object Detection in Aerial Images
- 融合空间与频域信息，精修低层特征图
- 在AI-TOD-v2数据集上超越现有最先进模型
- 适合遥感图像中微小目标检测任务
微小目标检测在遥感智能解译中至关重要，因这些目标常携带关键下游应用信息。然而，由于像素信息极度有限且目标密度变化显著，主流基于Transformer的检测器常出现收敛缓慢和查询-目标匹配不准的问题。为此，我们提出D$^3$R-DETR，一种基于DETR并引入双域密度精修的新方法。通过融合空间与频率域信息，该方法精修低层特征图，并利用其丰富细节预测更准确的目标密度图，从而引导模型精准定位微小目标。在AI-TOD-v2数据集上的大量实验表明，D$^3$R-DETR优于现有最先进的微小目标检测器。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。