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基于改进卷积神经网络的车型识别.docx

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基于改进卷积神经网络的车型识别 Title:ImprovingConvolutionalNeuralNetworksforCarClassification Abstract: Withtherapidadvancementofdeeplearningtechniques,convolutionalneuralnetworks(CNNs)havebecomeapowerfultoolforimageclassificationtasks.Inthispaper,weaimtoimprovetheaccuracyofcarclassificationusinganenhancedCNNmodel.Weproposeseveralstrategiestoenhancethenetworkarchitecture,datapreprocessing,andtrainingprocess.Theseimprovementsarevalidatedthroughextensiveexperimentsonacardataset,demonstratingtheireffectivenessinenhancingcarrecognitionperformance. 1.Introduction 1.1Background Carclassificationplaysacrucialroleinvariousapplications,suchastrafficmanagement,autonomousdriving,andsurveillancesystems.Theaccurateidentificationofcarmodelsfromimagesposesasignificantchallengeduetovariationsinviewpoints,lightingconditions,andocclusions.ConvolutionalNeuralNetworks(CNNs)haveshownpromisingperformanceinimageclassificationtasks,motivatingustoexploretheirpotentialforcarrecognition. 1.2Objectives TheobjectiveofthisresearchistoimprovetheaccuracyofcarclassificationusinganenhancedCNNmodel.Weaimtoexplorestrategiesforoptimizingthenetworkarchitecture,datapreprocessing,andtrainingprocess. 2.RelatedWork ThissectionreviewspreviousstudiesoncarclassificationandintroducestherelevantliteratureonimprovingCNNs.Itcoversacomprehensiveanalysisofvariousapproaches,suchastransferlearning,dataaugmentation,andarchitectureenhancements. 3.Methodology 3.1Dataset Weutilizealarge-scalecardataset,consistingofimagescollectedfromvarioussources,totrainandevaluateourCNNmodel.Thedatasetcontainsadiverserangeofcarmodels,makingitsuitablefortrainingarobustcarclassifier. 3.2EnhancedCNNArchitecture WeproposeanimprovedCNNarchitectureforcarclassification.Thisarchitectureincorporatesseveralmodifications,suchasincreaseddepth,theuseofresidualconnections,andtheinclusionofattentionmechanisms.Theseenhancementsaimtocapturemoredetailedanddiscriminativefeaturesfromcarimages,leadingtoimprovedclassificationaccuracy. 3.3DataPreprocessing Toimprovetheperfo