Density-Driven Network for Tiny Object Detection
Paper#Computer Vision, Object Detection, Remote Sensing🔬 Research|Analyzed: Jan 3, 2026 16:18•
Published: Dec 28, 2025 14:27
•1 min read
•ArXivAnalysis
This paper addresses the challenging problem of detecting dense, tiny objects in high-resolution remote sensing imagery. The key innovation is the use of density maps to guide feature learning, allowing the network to focus computational resources on the most relevant areas. This is achieved through a Density Generation Branch, a Dense Area Focusing Module, and a Dual Filter Fusion Module. The results demonstrate improved performance compared to existing methods, especially in complex scenarios.
Key Takeaways
- •Proposes DRMNet, a novel architecture for detecting dense tiny objects.
- •Utilizes density maps to guide feature learning and focus computational resources.
- •Employs a Density Generation Branch, Dense Area Focusing Module, and Dual Filter Fusion Module.
- •Achieves state-of-the-art performance on AI-TOD and DTOD datasets.
Reference / Citation
View Original"DRMNet surpasses state-of-the-art methods, particularly in complex scenarios with high object density and severe occlusion."