火力与指挥控制2026，Vol.51Issue(4)：61-73,13.DOI:10.3969/j.issn.1002-0640.2026.04.008
基于RLSW-YOLOv8n的无人机航拍小目标检测模型
Small Object Detection Model of UAV Aerial Imagery Based on RLSW-YOLOv8n
摘要
Abstract
Aiming at the problems of low accuracy and large parameters in small object detection models for UAV aerial imagery,this paper proposes the RLSW-YOLOv8n model.The C2f-RG module is constructed in the backbone network to enhance feature extraction capability.LCFFN-P2 is designed to reconstruct the neck network,achieving multi-scale feature fusion while reducing parameters.The SWSimAM module is proposed to highlight the features of small objects.Experimental results on the VisDrone2019 dataset show that compared with the original model,the improved model increases detection accuracy by 8.1%and reduces parameters by 32.2%.It also outperforms several existing state-of-the-art methods.关键词
YOLOv8n/无人机/小目标检测/多尺度特征融合/SWSimAMKey words
YOLOv8n/UAV/small object detection/multi-scale feature fusion/SWSimAM分类
信息技术与安全科学引用本文复制引用
王统,安洋,赵利辉,孟迪..基于RLSW-YOLOv8n的无人机航拍小目标检测模型[J].火力与指挥控制,2026,51(4):61-73,13.基金项目
山西省青年科学研究项目(202203021212114) （202203021212114）
中北大学第20届研究生科技立项资助项目(20242058) （20242058）
