HSFANet: Hierarchical Scale-Sensitive Feature Aggregation Network for Small Object Detection in UAV Aerial Images

Abstract

Small object detection in aerial images, particularly from Unmanned Aerial Vehicle (UAV) platforms, remains a significant challenge due to limited object resolution, dense scenes, and background interference. However, existing small object detectors often overlook making full use of hierarchical features and inevitably introduce noise interference because of hierarchical upsampling operations, and commonly used loss metrics lack sensitivity to scale information; these two issues jointly lead to performance deterioration. To address these issues, we propose Hierarchical Scale-Sensitive Feature Aggregation Network (HSFANet), a novel framework that conducts robust cross-layer feature interaction to perceive the small objects’ position information in hierarchical feature pyramids and enforces the model to balance the multi-scale prediction heads for accurate instances localization. HSFANet introduces a Dynamic Position Aggregation (DPA) module to explicitly enhance the object area in both shallow and deep layers, which is capable of exploiting the complementarily salient representation of the small objects. Additionally, an efficient Scale-Sensitive Loss (SSL) is proposed to balance the small object detection outputs in hierarchical prediction heads, thereby effectively improving the performance of small object detection. Extensive experiments on two challenging UAV benchmarks, VisDrone and UAVDT, demonstrate that HSFANet achieves state-of-the-art (SOTA) results, with a 1.3% gain in overall average precision (AP) and a notable 2.2% improvement in AP for small objects on VisDrone. On UAVDT, HSFANet outperforms previous methods by 0.3% in overall AP and 16.7% in small object AP. These results highlight the effectiveness of HSFANet in enhancing small object detection performance in complex aerial imagery, making it well suited for practical UAV-based applications.