现代电子技术2026，Vol.49Issue(19)：37-45,9.DOI:10.16652/j.issn.1004-373X.2026.19.006
基于改进YOLOv11n的无人机图像小目标检测算法
An improved YOLOv11n-based algorithm for small object detection in UAV images
摘要
Abstract
In view of the excessive small objects,complex backgrounds,large area of occlusion and limited computing resources in aerial images of unmanned aerial vehicles(UAVs),this paper proposes an algorithm DCC-YOLOv11n based on improved YOLOv11n.Firstly,an improved dilated convolution module C3k2 is introduced into this model to enhance the effect of high-level feature extraction,expand the receptive field,and generate feature maps with richer feature information.Secondly,a context-guided module is introduced into the model to suppress background noise and improve the model 's focus on target regions.Thirdly,inspired by the cross-channel feature modulation(CCFM)mechanism,a channel-adaptive module is integrated into the feature fusion stage to enhance both the model's cross-channel information interaction capability and nonlinear representation capability.Experiments demonstrate that,compared with the baseline YOLOv11n,DCC-YOLOv11n reduces the number of parameters by 8.8%on the VisDrone2019 dataset,with a compact model size of only 5.2 MB.While maintaining a lightweight design,this model sees improvements in precision,recall,mAP@0.5,and mAP@0.5:0.95 of 2.8%,2.6%,2.6%,and 1.8%,respectively,achieves 1.9%and 0.7%gains in mAP@0.5 and mAP@0.5:0.95,respectively,on the HIT-UAV dataset,and a 0.8%improvement in mAP@0.5 on the FOTL_Drone dataset.The above fully validates the effectiveness and generalizability of the proposed model.Overall,DCC-YOLOv11n significantly alleviates the issues of false positives and missed detections commonly observed in traditional UAV-based object detection methods,so it has a certain practicability.关键词
小目标检测/无人机/轻量化网络/YOLOv11n/膨胀卷积/多尺度特征融合/上下文引导模块/跨通道特征调制Key words
small object detection/UAV/lightweight network/YOLOv11n/dilated convolution/multi-scale feature fusion/context-guided module/cross-channel feature modulation分类
信息技术与安全科学引用本文复制引用
张攀峰,戴泽,程小辉,杜慧..基于改进YOLOv11n的无人机图像小目标检测算法[J].现代电子技术,2026,49(19):37-45,9.基金项目
广西重点研发计划(桂科AB24010338) （桂科AB24010338）
桂林市科学技术研究开发项目(20220138-1) （20220138-1）
广西壮族自治区信息中心技术服务项目(H20240242) （H20240242）
