Hierarchical Attention-Driven Detection of Small Objects in Remote Sensing Imagery

Accurate detection of small objects in remote sensing imagery remains challenging due to their limited texture, sparse features, and weak contrast. To address this, an enhanced small object detection model integrating top–down and bottom–up attention mechanisms is proposed. First, we design two statistical model-constrained feature pre-extraction networks to enhance the spatial patterns of small objects before feeding them into the backbone network. Next, a top–down attention mechanism followed by an overview-then-refinement process is employed to guide region-level feature extraction. Finally, a bottom–up feature fusion strategy is utilized to integrate micro features and macro structural features in a bottom–up manner, enhancing the representational capacity of limited features for small objects. Evaluations on the AI-TOD and SODA-A datasets show that our method outperforms existing benchmark models. On the AI-TOD dataset, it improves AP and AP0.5 by 0.3% and 2.7%, respectively. More notably, on the more challenging SODA-A dataset, it achieves significant gains of 0.5% in AP and 1.4% in AP0.5. These consistent enhancements across different datasets verify the effectiveness of our method in boosting detection performance, particularly for small targets.