@article{Shi2026, 
author = {Changzhu Shi and Hongmei Liu},
title = {DSGF-Net: A Dense-SE Gated-Fusion Architecture for High-Accuracy Small Object Detection in UAV Imagery},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {2},
pages = {26},
keywords = {UAV, small object detection, YOLOv10, feature fusion, attention mechanism, deep learning},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.074281},
doi = {10.32604/cmc.2026.074281},
abstract = {To address the critical challenges of small object detection in UAV imagery, this paper proposes DSGF-Net (Dense-SE Gated-Fusion Network), an enhanced architecture built upon YOLOv10. It integrates a Dense SE Network (DSENet) backbone, an Adaptive Gated Fusion (AGF) module, and a Channel-Spatial Attention (CSA) mechanism. Extensive experiments on VisDrone2019-DET and CODrone demonstrate that DSGF-Net achieves substantial mAP@0.5 improvements of 5.12% and 2.36% over the YOLOv10n baseline.}
}