基于改进YOLOv8n的车辆及行人检测

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DOI：10.16652/j.issn.1004-373x.2025.17.006引用格式：，，.基于改进YOLOv8n的车辆及行人检测[J].现代电子技术，2025，48（17）：35-40.

关键词：YOLOV8n；车辆行人检测；小目标检测；损失函数；SPPF；C2f中图分类号：TN911.73-34；TP391.41 文献标识码：A 文章编号：1004-373X（2025）17-0035-06

Vehicleand pedestrian detectionbased on improved YOLOv8nalgorithm

ZHOU Jianxin，HAO Yingjie，HOU Zichuan （CollegeofElectricalEngineering，NorthChinaUniversityofScienceandTechnologyTangshanO63210，China）

Abstract：Inview of theobject oclusion，overlap，and small object missing in roadscene detection，avehicleand pedestrianobjectdetectionmodelbasedontheimprovedYOLOv8nalgorithmhasbeenproposed.Firstly，anew pyramidpooling layerstructurecaled SPPF-CREPisdesigned toreplace theoriginal network'spyramidpoling layerstructure SPF，nhancing themodel'seficiencyand performance during trainingand inference.Secondly，asmall object detection head （P2） is added to improvethenetwork'sdetectioncapabilityforsmallobjects.Then，theonlineconvolutionalre-parameterization（OREPA）is integratedintotheC2f moduletoenhancetheauracyrateandeficiencyofvehicleandpedestriandetectioninhigh-density environments.Finally，the WIoU- v2 isadopted asan alternative loss function to achieve higher localization accuracy. ExperimentalresultsontheKITTIvehicledetectiondatasetdemonstratesthat，incomparisonwiththeoriginalalgorithm，the accuracyrateof thedetectionoftheimprovedalgorithmisincreasedby3.6%，andthemeanaverageprecision（mAP）is enhanced by 4.2% .This experiment has proven its eficiency and superiority in the detection of vehicles and pedestrians.

Keywords：YOLOV8n;vehicleand pedestrian detection;small object detection;loss function;SPPF; C2f

0 引言

在道路目标检测任务中，传统的目标检测特征泛化能力弱，对复杂场景的性能表现较差1，并且存在准确率低和检测速度缓慢等问题。（剩余8220字）