计算机工程与应用2025，Vol.61Issue(14)：88-100,13.DOI:10.3778/j.issn.1002-8331.2502-0223
DMF-YOLOv11:基于改进YOLOv11n的无人机航拍图像目标检测算法
DMF-YOLOv11:Target Detection Algorithm for UAV Images Based on Improved YOLOv11n
摘要
Abstract
To address the insufficient detection accuracy caused by dense small-sized targets,significant multi-scale varia-tions,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.关键词
小目标检测/YOLOv11/特征金字塔/感受野/特征调制Key words
small target detection/YOLOv11/feature pyramid/receptive field/feature modulation分类
信息技术与安全科学引用本文复制引用
贺智轩,陈里里,王翔,李荣华..DMF-YOLOv11:基于改进YOLOv11n的无人机航拍图像目标检测算法[J].计算机工程与应用,2025,61(14):88-100,13.基金项目
重庆市技术创新与应用发展专项重大项目(CSTB2024TIAD-STX0027) （CSTB2024TIAD-STX0027）
重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0075) （CSTB2022TIAD-KPX0075）
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0801). （CSTB2022NSCQ-MSX0801）
