无线电工程2025，Vol.55Issue(8)：1560-1570,11.DOI:10.3969/j.issn.1003-3106.2025.08.002
基于YOLO11的无人机航拍图像小目标检测算法
Small Target Detection Algorithm for UAV Aerial Images Based on YOLO11
摘要
Abstract
To address the challenges of tiny target size,dense distribution,blurred feature information,and complex background interference in small target detection of UAV aerial images,the YOLO11 model is improved.The enhancements focus on three key aspects:a progressive multi-scale feature extraction module called Channel Split Multi-scale Aggregation Convolution(CSMAConv)is designed to modify the original C3k2 module.By using channel splitting and a cascaded multi-scale convolution structure,the original feature information is preserved and the receptive field is expanded,which significantly enhances the ability to capture multi-scale detailed features.A new feature pyramid structure called Hierarchical Efficient Fusion Feature Pyramid Network(HEFFPN)is proposed.By constructing additional cross-scale feature fusion paths and introducing the Selective Boundary Aggregation(SBA)module,it more frequently integrates multi-scale information and improves the feature fusion capability.A Shared Enhanced Detection-Head(SED-Head)is designed to reduce computational complexity while improving the detection efficiency of small targets.Experiments on the VisDrone-DET2019 dataset demonstrate that,compared to YOLO11s,the improved model reduces the number of parameters by 51.1%,while increasing mAP@0.5 by 0.099 and mAP@0.5:0.95 by 0.07,validating the effectiveness of the proposed method.关键词
YOLO11/小目标检测/特征融合/无人机航拍图像Key words
YOLO11/small target detection/feature fusion/UAV aerial images分类
信息技术与安全科学引用本文复制引用
贾星宇,李大鹏..基于YOLO11的无人机航拍图像小目标检测算法[J].无线电工程,2025,55(8):1560-1570,11.基金项目
国家自然科学基金(62371245) National Natural Science Foundation of China(62371245) （62371245）
