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<p>Small object detection in unmanned aerial vehicle (UAV) aerial imagery faces substantial challenges due to small target scales, complex backgrounds, noise interference, and so on. To enhance multi-scale feature representation and detection efficiency, this paper proposes MSEF-YOLO11s. Specifically, we first design a lightweight partial multi-scale (LPMS) module, which effectively aggregates cross-scale information and enhances multi-scale representations in the backbone for small objects. Secondly, to dynamically adjust feature weights and mitigate feature conflicts in the neck, we devise a multi-scale boundary-semantic alignment (MS-BSA) based on adaptive attention, which can further avoid computational redundancy for sufficient fusion. Finally, a lightweight shared detail detection head (LSDDH) replaces the decoupled head structure with shared convolutional layers, resolving the issue of parameter explosion associated with adding a dedicated small object detection head. Experimental results demonstrate the effectiveness of the proposed model. Specifically, compared to the baseline YOLO11s, MSEF-YOLO11s achieves an improvement of 6.6% in mAP50 on the VisDrone2019 test set, with only 4.4M increase in parameters. Furthermore, mAP50 on the TinyPerson test set increases from 22.8% to 28.1%, confirming the model’s strong generalization capability.</p>

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MSEF-YOLO11s: a multi-scale extraction and fusion network for small target detection in drone imagery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月20日

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  • Kai Zhang,
  • Pengcheng Zhang,
  • Farhan Ullah,
  • Yue Zhao

摘要

Small object detection in unmanned aerial vehicle (UAV) aerial imagery faces substantial challenges due to small target scales, complex backgrounds, noise interference, and so on. To enhance multi-scale feature representation and detection efficiency, this paper proposes MSEF-YOLO11s. Specifically, we first design a lightweight partial multi-scale (LPMS) module, which effectively aggregates cross-scale information and enhances multi-scale representations in the backbone for small objects. Secondly, to dynamically adjust feature weights and mitigate feature conflicts in the neck, we devise a multi-scale boundary-semantic alignment (MS-BSA) based on adaptive attention, which can further avoid computational redundancy for sufficient fusion. Finally, a lightweight shared detail detection head (LSDDH) replaces the decoupled head structure with shared convolutional layers, resolving the issue of parameter explosion associated with adding a dedicated small object detection head. Experimental results demonstrate the effectiveness of the proposed model. Specifically, compared to the baseline YOLO11s, MSEF-YOLO11s achieves an improvement of 6.6% in mAP50 on the VisDrone2019 test set, with only 4.4M increase in parameters. Furthermore, mAP50 on the TinyPerson test set increases from 22.8% to 28.1%, confirming the model’s strong generalization capability.