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Small object detection in UAV aerial imagery encounters critical challenges including extremely small target sizes, complex background interference, and insufficient feature representation. Addressing the limitations of existing RT-DETR models in small object feature extraction and multi-scale fusion, this paper proposes an adaptive multi-scale gated enhancement fusion DETR (MGEF-DETR). A multi-order cross-stage gated aggregation (MCGA) module is designed to achieve selective enhancement of small object texture features through adaptive gating mechanisms. A Micro-OmniPyramid feature pyramid is constructed by integrating space-to-depth (SPD) convolution sparse encoding and cross-stage enhanced spectral kernel (CESK) modules, establishing lossless transmission pathways for small object features. An enhanced feature correlation (EFC) module is introduced to optimize cross-scale feature fusion through grouped attention and multi-level reconstruction strategies. An inner-modified penalty distance IoU (IMIoU) loss function is designed to enhance boundary regression sensitivity for small objects. Experimental results on the VisDrone2019 dataset demonstrate that MGEF-DETR achieves improvements of 3.9% and 3.1% in mAP@0.5 and mAP@0.5:0.95 metrics respectively compared to the baseline RT-DETR, while reducing parameters by 13.6%. Validation on TinyPerson and CODrone datasets further confirms the generalization capability of the algorithm, indicating significant improvements in both accuracy and efficiency for small object detection in aerial scenarios while maintaining lightweight characteristics.

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无人机航拍图像中的小目标检测面临目标尺寸微小、背景干扰复杂、特征表达不充分等关键技术挑战。针对现有RT-DETR模型在小目标特征提取和多尺度融合方面的局限性，提出一种自适应多尺度门控增强融合检测模型（MGEF-DETR）。该方法通过设计多阶跨阶段门控聚合模块（MCGA），通过自适应门控机制实现小目标纹理特征的选择性增强；构建Micro-OmniPyramid小目标特征金字塔，集成SPD卷积稀疏编码和跨阶段增强空间核模块（CESK），建立小目标特征的无损传递通路；引入增强特征关联模块EFC，通过分组注意力和多级重建策略优化跨尺度特征融合；设计内部修正惩罚距离IoU损失函数（IMIoU），增强边界回归对小目标的敏感性。在VisDrone2019数据集上的实验结果表明，MGEF-DETR相比基线模型RT-DETR在mAP@0.5和mAP@0.5：0.95指标上分别提升3.9%和3.1%，同时参数量减少13.6%。在TinyPerson和CODrone数据集上的验证进一步证实了算法的泛化能力，表明该方法在保持轻量化的同时显著提升了航拍场景下小目标检测的精度和效率。

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侯林杰，硕士研究生，主要研究方向为计算机视觉、目标检测。E-mail: 2024720774@yangtzeu.edu.cn

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侯林杰，硕士研究生，主要研究方向为计算机视觉、目标检测。E-mail: 2024720774@yangtzeu.edu.cn

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卢承方，硕士研究生，主要研究方向为深度学习、目标检测。E-mail: 2023710700@yangtzeu.edu.cn

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卢承方，硕士研究生，主要研究方向为深度学习、目标检测。E-mail: 2023710700@yangtzeu.edu.cn

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