提升小目标检测效率，通过智能分配注意力和查询优化性能。
Dome-DETR: DETR with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection
- 根据目标密度动态调整特征与查询分布，减少冗余计算。
- 在AI-TOD-V2和VisDrone数据集上分别提升3.3和2.5点AP。
- 适合无人机、遥感等需要高效小目标识别的场景。
小目标检测在无人机监控、遥感和自动驾驶系统中至关重要，可实现对广阔区域中微小目标的识别。然而，现有方法因特征处理冗余和查询分配僵化，导致计算效率低下。为此，我们提出Dome-DETR，一种基于密度导向的特征-查询调控框架，以实现高效的细粒度目标检测。为减少特征冗余，引入轻量级密度聚焦提取器（DeFE）生成紧凑的前景掩码；利用该掩码，采用掩码窗口注意力稀疏化（MWAS），通过稀疏注意力聚焦于最具信息量的区域。此外，提出渐进式自适应查询初始化（PAQI），按空间区域动态调节查询密度，优化查询分配。大量实验表明，Dome-DETR在AI-TOD-V2和VisDrone数据集上分别取得+3.3 AP和+2.5 AP的性能提升，同时保持低计算复杂度与紧凑模型规模。代码已开源：https://github.com/RicePasteM/Dome-DETR。
原文摘要 · Abstract (English)
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.
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