HSAR-DETR: Hierarchical Spatial–Frequency Attention Network for UAV Small Object Detection

Abstract

Small object detection in UAV remote sensing imagery plays a crucial role in applications such as infrastructure inspection, disaster assessment, and precision agriculture, where targets of interest frequently occupy fewer than 32×32 pixels under large ground sampling distance variation and complex cluttered backgrounds. Existing methods still face three main challenges in UAV small-object detection: fine-grained detail loss caused by repeated downsampling, feature inconsistency during cross-scale fusion, and unstable boundary regression in densely distributed aerial scenes. To address these issues, this paper proposes HSAR-DETR, a detection framework that jointly improves hierarchical feature representation, cross-scale refinement, and geometry-aware localization. Specifically, a Hierarchical Enhancement Network (HENet) is introduced to preserve shallow spatial details while strengthening deep semantic-context representation. A Dual-Stream Feature Refinement module (DSFR) is designed at the P4-to-P3 fusion stage, combining spatial-domain structural modeling with frequency-domain phase refinement to improve cross-scale feature consistency. A Coordinate-Guided Adaptive Convolution module (CGAC) is further deployed before the detection head, converting coordinate-guided offset magnitudes into modulation weights for adaptive feature recalibration and improved localization stability. In addition, a conventional high-resolution P2 detection branch is incorporated to enhance small-object representation. Experimental results on the VisDrone, RSOD, and TinyPerson datasets demonstrate improved detection performance. On the VisDrone validation set, HSAR-DETR achieves 50.8% mAP50 and 31.4% mAP50:95, outperforming the RT-DETR baseline by 4.2 and 3.0 percentage points, respectively.