Learning Where to Focus: Density-Driven Guidance for Detecting Dense Tiny Objects
This work addresses the challenge of detecting dense tiny objects in remote sensing for applications like surveillance or monitoring, representing an incremental improvement over prior methods by introducing density-guided mechanisms.
The paper tackles the problem of detecting dense clusters of tiny objects in high-resolution remote sensing imagery, where existing methods fail to adaptively focus on dense regions, and proposes DRMNet, which uses density maps to guide feature learning and achieves state-of-the-art performance on datasets like AI-TOD and DTOD, especially in complex scenarios with high density and occlusion.
High-resolution remote sensing imagery increasingly contains dense clusters of tiny objects, the detection of which is extremely challenging due to severe mutual occlusion and limited pixel footprints. Existing detection methods typically allocate computational resources uniformly, failing to adaptively focus on these density-concentrated regions, which hinders feature learning effectiveness. To address these limitations, we propose the Dense Region Mining Network (DRMNet), which leverages density maps as explicit spatial priors to guide adaptive feature learning. First, we design a Density Generation Branch (DGB) to model object distribution patterns, providing quantifiable priors that guide the network toward dense regions. Second, to address the computational bottleneck of global attention, our Dense Area Focusing Module (DAFM) uses these density maps to identify and focus on dense areas, enabling efficient local-global feature interaction. Finally, to mitigate feature degradation during hierarchical extraction, we introduce a Dual Filter Fusion Module (DFFM). It disentangles multi-scale features into high- and low-frequency components using a discrete cosine transform and then performs density-guided cross-attention to enhance complementarity while suppressing background interference. Extensive experiments on the AI-TOD and DTOD datasets demonstrate that DRMNet surpasses state-of-the-art methods, particularly in complex scenarios with high object density and severe occlusion.