YOLO-SWIFT: A Small-object Wavelet-Integrated Feature Transform Network for Object Detection in UAV Aerial Imagery

Abstract

Accurate detection of small-scale targets in UAV aerial imagery remains challenging due to severe scale variation, background interference, and feature degradation caused by repeated downsampling. To address these issues, this study proposes YOLO-SWIFT, a wavelet-integrated feature transformation network designed for small-object detection. First, a Position-Aware Coordinate Downsampling module is developed to preserve critical spatial information during feature compression through coordinate attention and skip connections. Second, a High-Resolution Feature Aggregation Network is introduced to establish an additional high-resolution detection branch for enhanced cross-scale feature fusion. Third, a Wavelet Bottleneck Enhancement module incorporating multi-level wavelet decomposition and a High-Frequency Retention pathway is designed to improve fine-detail representation while expanding the effective receptive field. Finally, an Adaptive Scale-aware Regression IoU loss function is proposed to dynamically balance localization and shape-consistency constraints for small targets. Experimental evaluation on the VisDrone2019 benchmark demonstrates that YOLO-SWIFT achieves 38.7% mAP50 and 22.5% mAP50:95. The proposed framework provides an effective solution for intelligent aerial sensing and offers potential applications in electromagnetic imaging, remote sensing interpretation, and autonomous surveillance systems.