基于BP神经网络的车牌识别技术研究(英文版)

ResearchonlicenseplaterecognitiontechnologybasedonBPneuralnetworkWiththecontinuousdevelopmentofscienceandtechnology,meansoftrafficmanagementisfrommanualmanagementgraduallytransformedintoautomaticallyorsemiautomatically,licenseplaterecognitionasoneofthekeyandhotissuesintheresearchfieldofmoderntrafficengineeringbymoreandmorepeople'sattention.Inrecentyears,neuralnetworkshavebeenappliedinmanyfields,andthecharacteristicsofneuralnetworksareusedtomakethecharacterrecognitionbasedonBPneuralnetwork.Thisarticlethroughtoinlicenseplaterecognitionsystemimagepreprocessing,fourkeysteps:licenseplatelocation,charactersegmentationandcharacterrecognitionofproposedakindoflicenseplatecharactersbasedonneuralnetworkrecognitionalgorithm.Usedthismethodoflicenseplateimageexperimentswereconductedtoextractthefeatureofthelicenseplatecharactersample,andundertheenvironmentofMATLABonthelicenseplatecharacterrecognitionwassimulated.Theresultsshowedthatthisalgorithmthecharactersonthelicenseplatelocationandsegmentationhasgoodeffect,thelicenseplatecharacterrecognitionwithcertainaccuracy.Keywords:BPneuralnetwork;licenseplatelocation;licenseplaterecognition;charactersegmentation;characterrecognition1IntroductionWiththeincreaseofthenumberofcars,therearetrafficcongestionintheworld.Inordertosolvethisproblem,manycitieswillbewidenedlane,butstillfarfromsolvingtheproblem.Nottoincreasetheexistingroadfacilities,howtoimprovetheefficiencyoftransportationhasbecomethefocusofresearchintheworld.Intelligenttransportationsystem(Intelligent-TransportationSystemITS)isthemaindevelopmenttrendofthefuturetrafficregulationsystem.Vehiclelicenseplaterecognitiontechnology(License-PlateRecognitionLPR)isoneofthecoretechnologiesinITS.Therefore,theresearchanddevelopmentoflicenseplaterecognitionsystemisofgreatpracticalvalueforthedevelopmentofChina'strafficmanagementfield.Atpresent,therearestillmanyproblemsinthelicenseplaterecognitionsystem.Recognitionrateisnotpossibletodoonehundredpercent,butwiththedeepeningofresearch,licenseplaterecognitiontechnologywillgraduallymature.Thedevelopmentofmodernintelligenttransportation,makeithasgreatpotentialforapplication,abroadermarket.Atthesametime,neuralnetworkinclassificationproblemsgetwidelyused,forlicenseplaterecognitionproblem,wemustfirstfindthelicenseplatefeatures,andcorrespondingevaluationdata,usingthesedatatotrainneuralnetwork.Becausetheartificialneuralnetworkhasthecharacteristicsofparallelprocessing,distributedstorageandfaulttolerance,itiswidelyusedintheLPRsystem.Theparallelismofthestructuremakestheinformationstorageoftheneuralnetworkadoptthedistributedmode,thatis,thelicenseplatecharacterinformationisnotstoredinapartofthenetwork,butisdistributedinthenetworkofalltheconnections.Thesefeaturesareboundtomaketheneuralnetworkinthelicenseplaterecognitionofthetwoaspectsoftheperformanceofagoodfaulttolerance:(1)becauseofthedistributedstorageofthecharactercharacteristicinformation,thewholeperformanceofthevehiclelicenseplaterecognitionsystemwillnotbeaffectedwhensomeoftheneuronsinthenetworkaredamaged.(2)neuralnetworkthroughprestoredinformationandlearningmechanismsforadaptivetraining,cannevercompletelicenseplateinformationandnoiseofthelicenseplateimagebyLenovotorestorefullmemoriesoftheoriginal,inordertoachievethecorrectidentificationoftheincompleteinputinformation.Basedontheabovecharacteristics,theapplicationofartificialneuralnetworkinthevehiclelicenseplaterecognitionsystemhasgreatresearchvalue.2introductiontheprincipleofBPneuralnetworkBP(backpropagation)networkisproposedthescientistsgroup1986byRumelhartandMcCellandheaded,isakindoferrorinversepropagationtrainingalgorithmforthemultilayerfeedforwardnetworkandiscurrentlythemostwidelyusedmodelsofneuralnetwork.BPnetworkcanlearnandstorealotofinput-outputmodelmapping,withoutpriormathematicsdescribingthismappingequation.Itslearningruleisthesteepestdescentmethodisusedtoadjusttheweightsandthresholdsofthenetworkthroughtheback-propagationnetwork,theminimumerrorsumofsquares.BPneuralnetworktopology,includinginputlayer,hiddenlayer(input)(hidelayer)andoutputlayer(outputlayer).2.1BPalgorithmTheerrorback-propagationalgorithm(BPalgorithm)ofthelearningprocess,bythereverseforwardpropagationanderrorinformationtransmissionconsistsoftwoprocesses.Inputlayerneuronsreceivestheinputinformationfromtheoutsideworld,andpassedtothemiddlelayerneurons;intermediatelayerisinternalinformationprocessinglayerandisresponsiblefortheinformationtransform,accordingtothedemandoftheinformationchanges,themiddlelayercanbedesignedforsinglehiddenlayerormultihiddenlayerstructure;thelasthiddenlayertransfertooutputlayerneurons,afterfurtherprocessing,tocompletealearningforwardpropagationprocess,fromtheoutputlayeroutputtotheoutsideinformationprocessingresults.Whentheactualoutputisnotinconformitywiththeexpectedoutput,thereversepropagationphaseoftheerrorisentered.Theerroriscorrectedbytheoutputlayer,andtheweightofeachlayeriscorrectedbytheerrorgradientdescentmethod.Thecycleofinformationforwardpropagationanderrorbackpropagationprocess,theconstantadjustmentoft