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Article | Open Access

DSGF-Net: A Dense-SE Gated-Fusion Architecture for High-Accuracy Small Object Detection in UAV Imagery

Changzhu Shi, Hongmei Liu( )
School of Mathematical Sciences, Dalian Minzu University, Dalian, China
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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.

Keywords

UAVsmall object detectionYOLOv10feature fusionattention mechanismdeep learning

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Computers, Materials & Continua
Volume 88 Issue 2,
June 2026
Article number: 26
DOI: 10.32604/cmc.2026.074281

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Shi C, Liu H. DSGF-Net: A Dense-SE Gated-Fusion Architecture for High-Accuracy Small Object Detection in UAV Imagery. Computers, Materials & Continua, 2026, 88(2): 26. https://doi.org/10.32604/cmc.2026.074281

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Received: 07 October 2025
Accepted: 22 December 2025
Published: 15 June 2026
© The Author 2026.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.