高技术通讯2026，Vol.36Issue(2)：179-190,12.DOI:10.3772/j.issn.1002-0470.2026.02.007
基于YOLOv8-FRX的无人机小目标检测改进算法
Research on an improved YOLOv8-FRX algorithm for UAV small object detection
摘要
Abstract
To address the issues of dense small object distribution,complex backgrounds,and high false detection and missed detection rates of small objects in traditional YOLO(you only look once)algorithms for drone-captured ima-ges,an improved object detection algorithm,YOLOv8-FRX(feature refinement and eXtended detection),is pro-posed.This method enhances model performance through three main improvements:first,introducing reparameter-ised cross stage partial network(RepCSP)in the backbone network and using reparameterized convolution(Rep-Conv)on the gradient circulation branch to enhance the feature extraction capability while reducing the number of parameters;second,designing a small object enhanced pyramid(SOEP)to improve the efficiency of small object feature capture while maintaining computational efficiency;and third,adopting the Wise-IoU v3(wise intersection over union v3)strategy to optimize gradient gain allocation and enhance the accuracy of bounding box regression.Experimental results on the VisDrone dataset demonstrate that the proposed method improves the mAP50 by 4.0%compared to the baseline model while reducing parameters by 3.6%.In generalization experiments on the UAVDT(unmanned aerial vehicle detection and tracking)dataset,detection accuracy increased by 1.7%,validating the method's generality and effectiveness.These improvements not only enhance the detection capability for small ob-jects but also balance accuracy and efficiency,providing a superior solution for object detection in drone-captured images.关键词
无人机/小目标检测/重参数化跨阶段部分网络/小目标增强金字塔/加权交并比Key words
unmanned aerial vehicle/small object detection/reparameterized cross stage partial network/small object enhanced pyramid/wise intersection over union引用本文复制引用
林波,黄洪琼..基于YOLOv8-FRX的无人机小目标检测改进算法[J].高技术通讯,2026,36(2):179-190,12.基金项目
国家自然科学基金(62271303)资助项目. （62271303）
