Abstract: To address the insufficient detection accuracy caused by dense small-sized targets, significant multi-scale variations, and complex scene interference in drone aerial image target detection, this paper proposes an improved YOLOv11n-based algorithm named DMF-YOLOv11. Firstly, a dual bidirectional auxiliary feature pyramid network (DBAFPN) is designed as the Neck structure to enhance feature representation for extremely small and regular small targets through multi-level bidirectional feature fusion. Secondly, a multi-branch hybrid convolution (MBHConv) module is constructed to improve sensitivity toward small-scale targets using parallel heterogeneous convolutional paths. Finally, the self-modulating feature aggregation network (SMFANet) is deeply integrated with the backbone C3K2 module, proposing the C3K2_FMB block to collaboratively extract local details and non-global contextual features. Experiments on the VisDrone2019 dataset demonstrate that DMF-YOLOv11 achieves mAP50 and mAP50-95 scores of 46.2% and 28.4%, respectively, surpassing the baseline YOLOv11n by 11.5 and 8.3 percentage points. The recall rate increases by 9.4 percentage points to 44.6%. The improved algorithm effectively enhances target detection accuracy in drone aerial scenarios.

Key words: small target detection, YOLOv11, feature pyramid, receptive field, feature modulation

摘要： 针对无人机航拍视角下目标检测中存在的小尺寸目标密集、多尺度变化显著及复杂场景干扰导致的检测精度不足问题，提出一种基于YOLOv11n改进的无人机航拍图像目标检测算法DMF-YOLOv11。设计双重双向辅助特征金字塔网络（dual bidirectional auxiliary feature pyramid network，DBAFPN）作为Neck结构，通过多层级特征双向融合机制增强极小目标与常规小目标的特征表征能力；构建多分支混合卷积模块（multi-branch hybrid convolution，MBHConv），采用并行异构卷积路径提升模型对小尺度目标的感知灵敏度；将自调特征聚合网络（self-modulating feature aggregation network， SMFANet）与主干网络C3K2模块深度融合，提出C3K2_FMB模块以协同提取局部细节与非全局上下文特征。在VisDrone2019数据集上的实验表明，DMF-YOLOv11的mAP50与mAP50-95分别达到46.2%和28.4%，较基准模型YOLOv11n分别提升11.5和8.3个百分点，召回率提升9.4个百分点至44.6%。改进算法有效提升了无人机航拍场景下的目标检测精度。

关键词: 小目标检测, YOLOv11, 特征金字塔, 感受野, 特征调制