DRF-Net: Frequency-Guided Feature Reconstruction for Small Object Detection in Aerial Imagery

Small object detection in aerial imagery remains challenging due to limited spatial resolution, background clutter, and severe scale variation.Existing deep learning-based detectors often suffer from weakened shallow representations and insufficient cross-scale feature interaction, leading to missed detections and unstable localization in dense scenes.This work presents Dynamic Reconstruction and Fusion Network (DRF-Net), a frequencyguided feature reconstruction framework for small object detection.Built upon a one-stage detection paradigm, the proposed method introduces three key components: a frequency-guided channel-spatial augmentation (FCSA) module to enhance fine-grained representations, a multi-frequency reconstruction block (MFRB) to restore crossscale structural information, and a Dynamic Reconstruction Fusion Neck (DRF-Neck) to adaptively regulate multiscale feature aggregation.By jointly modeling high-and low-frequency components and integrating saliencyaware fusion mechanisms, the framework improves the preservation of small-object contours while suppressing redundant background responses.Extensive experiments conducted on the VisDrone2019 benchmark demonstrate that DRF-Net consistently outperforms the baseline detector in terms of detection accuracy, particularly for small and densely distributed objects, while maintaining real-time inference efficiency.Ablation studies further verify the complementary contributions of the proposed modules to feature representation and fusion stability.The results indicate that frequency-guided reconstruction and dynamic fusion provide an effective learning strategy for enhancing small-object detection performance in complex visual scenes.