STE-YOLO: A Dual Enhancement Architecture for Small Object Detection in Aerial Imagery

Object detection in aerial imagery faces unique challenges due to small object scales, ambiguous textures, and dense distributions. Traditional detection methods often struggle with preserving structural information of small targets and effectively utilizing both global context and local details. To address these limitations, we propose STE-YOLO Small Target Enhancement YOLO , featuring two key innovations: a Multi-level Content-Aware Feature Enhancement Module MCAFE that dynamically adjusts feature fusion strategies, and a Local-Enhance Global Attention LEGA module that effectively balances global context and local feature representation. Extensive experiments on VisDrone2019 dataset demonstrate that STE-YOLO significantly outperforms baseline models. Compared to YOLOv10, our method achieves improvements of 10.8 in mAP@0.5 and 11.0 in mAP@0.5:0.95 on VisDrone2019. Additionally, we conducted generalization experiments on DOTAv1.5 dataset, where our method also shows strong performance, demonstrating the robustness and adaptability of our approach across different aerial imagery scenarios while maintaining acceptable computational overhead.