Learning Where to Focus: Density-Driven Guidance for Detecting Dense Tiny Objects

TL;DR

This paper introduces DRMNet, a novel density-guided detection network that adaptively focuses on dense tiny objects in high-resolution imagery, significantly improving detection accuracy in occluded and crowded scenes.

Contribution

The paper proposes a density-driven guidance framework with novel modules for modeling object distribution, focusing dense regions, and enhancing multi-scale features, advancing dense tiny object detection.

Findings

DRMNet outperforms state-of-the-art methods on AI-TOD and DTOD datasets.

The density-guided modules improve detection in occluded and dense scenarios.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

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.