Dome-DETR: DETR with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection

• URL: http://arxiv.org/abs/2505.05741v1
• Date: Fri, 09 May 2025 02:44:06 GMT
• Title: Dome-DETR: DETR with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection
• Authors: Zhangchi Hu, Peixi Wu, Jie Chen, Huyue Zhu, Yijun Wang, Yansong Peng, Hebei Li, Xiaoyan Sun,
• Abstract summary: Dome-DETR is a novel framework with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection.<n>It 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.
• Score: 7.16574066661446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 will be released upon acceptance.

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