Dome-DETR：面向高效小目标检测的密度导向特征-查询操作的DETR
Dome-DETR: DETR with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection
AI总结：
Dome-DETR通过密度导向的特征-查询操作（包括DeFE、MWAS和PAQI）高效处理小目标检测，在AI-TOD-V2和VisDrone上分别提升3.3和2.5 AP，同时保持低计算复杂度和紧凑模型。
摘要：
小目标检测在无人机监控、遥感以及自主系统中发挥着至关重要的作用，能够在大范围场景中识别小目标。然而，现有方法由于冗余的特征处理和僵化的查询分配，导致特征利用效率低下且计算成本高昂。为解决这些挑战，我们提出了Dome-DETR，一种带有密度导向特征-查询操作的新颖框架，用于高效小目标检测。为减少特征冗余，我们引入了一个轻量级的密度-焦点提取器（DeFE），用于生成紧凑的聚类前景掩码。利用这些掩码，我们结合了掩码窗口注意力稀疏化（MWAS），通过稀疏注意力将计算资源集中在信息最丰富的区域。此外，我们提出了渐进式自适应查询初始化（PAQI），该机制自适应地调节跨空间区域的查询密度，以实现更好的查询分配。大量实验表明，Dome-DETR在AI-TOD-V2上实现了最先进的性能（+3.3 AP），在VisDrone上实现了+2.5 AP的提升，同时保持了较低的计算复杂度和紧凑的模型尺寸。代码可在 https://github.com/RicePasteM/Dome-DETR 获取。
英文摘要：
Tiny object detection plays a vital role in drone surveillance, remote sensing, and autonomous systems, enabling the identification of small targets across vast landscapes. However, existing methods suffer from inefficient feature leverage and high computational costs due to redundant feature processing and rigid query allocation. To address these challenges, we propose Dome-DETR, a novel framework with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection. To reduce feature redundancies, we introduce a lightweight Density-Focal Extractor (DeFE) to produce clustered compact foreground masks. Leveraging these masks, we incorporate Masked Window Attention Sparsification (MWAS) to focus computational resources on the most informative regions via sparse attention. Besides, we propose Progressive Adaptive Query Initialization (PAQI), which adaptively modulates query density across spatial areas for better query allocation. Extensive experiments demonstrate that Dome-DETR achieves state-of-the-art performance (+3.3 AP on AI-TOD-V2 and +2.5 AP on VisDrone) while maintaining low computational complexity and a compact model size. Code is available at https://github.com/RicePasteM/Dome-DETR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
