计算机工程与应用2025，Vol.61Issue(15)：124-131,8.DOI:10.3778/j.issn.1002-8331.2503-0274
改进YOLOv11的无人机小目标检测算法
Improved YOLOv11 Algorithm for Small Target Detection in UAVs
摘要
Abstract
To address the issues of small target detection tasks for unmanned aerial vehicles(UAVs),such as few pixels,large scale variations,and susceptibility to background interference,an improved algorithm based on YOLOv11 is pro-posed.Firstly,a new ELAN-DC module is designed to improve the backbone network,combining double convolution DC in the CBS module of the efficient layer aggregation network ELAN to enhance the feature extraction capability of back-bone part of the model.Secondly,a new global-to-local bidirectional feature fusion structure GLBiFPN is designed to improve the effect of multi-scale feature fusion.Finally,a dynamic detection head DyHead is introduced to further enhance the detection accuracy of the model.Experimental results show that on the VisDrone2019 dataset,the detection accuracy,mAP50 and mAP50-95,of the proposed algorithm has increased by 5.1 and 3.5 percentage points respectively,com-pared to YOLOv11n.关键词
YOLOv11/小目标/多尺度特征融合/无人机Key words
YOLOv11/small target/multi-scale feature fusion/unmanned aerial vehicles(UAVs)分类
信息技术与安全科学引用本文复制引用
刘玉萍,尚翠娟,李明明..改进YOLOv11的无人机小目标检测算法[J].计算机工程与应用,2025,61(15):124-131,8.基金项目
安徽省高校优秀青年科研项目(2022AH030109). （2022AH030109）
