基于BP神经网络的车牌识别技术研究(英文版).doc

基于BP神经网络的车牌识别技术研究(英文版).doc

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ResearchonlicenseplaterecognitiontechnologybasedonBPneuralnetwork

Withthecontinuousdevelopmentofscienceandtechnology,meansoftrafficmanagementisfrommanualmanagementgraduallytransformedintoautomaticallyorsemiautomatically,licenseplaterecognitionasoneofthekeyandhotissuesintheresearchfieldofmoderntrafficengineeringbymoreandmorepeople'sattention.Inrecentyears,neuralnetworkshavebeenappliedinmanyfields,andthecharacteristicsofneuralnetworksareusedtomakethecharacterrecognitionbasedonBPneuralnetwork.

Thisarticlethroughtoinlicenseplaterecognitionsystemimagepreprocessing,fourkeysteps:

licenseplatelocation,charactersegmentationandcharacterrecognitionofproposedakindoflicenseplatecharactersbasedonneuralnetworkrecognitionalgorithm.Usedthismethodoflicenseplateimageexperimentswereconductedtoextractthefeatureofthelicenseplatecharactersample,andundertheenvironmentofMATLABonthelicenseplatecharacterrecognitionwassimulated.Theresultsshowedthatthisalgorithmthecharactersonthelicenseplatelocationandsegmentationhasgoodeffect,thelicenseplatecharacterrecognitionwithcertainaccuracy.

Keywords:

BPneuralnetwork;licenseplatelocation;licenseplaterecognition;charactersegmentation;characterrecognition

1Introduction

Withtheincreaseofthenumberofcars,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.

2introductiontheprincipleofBPneuralnetwork

BP（backpropagation）networkisproposedthescientistsgroup1986byRumelhartandMcCellandheaded,isakindoferrorinversepropagationtrainingalgorithmforthemultilayerfeedforwardnetworkandiscurrentlythemostwidelyusedmodelsofneuralnetwork.BPnetworkcanlearnandstorealotofinput-outputmodelmapping,withoutpriormathematicsdescribingthismappingequation.Itslearningruleisthesteepestdescentmethodisusedtoadjusttheweightsandthresholdsofthenetworkthroughtheback-propagationnetwork,theminimumerrorsumofsquares.BPneuralnetworktopology,includinginputlayer,hiddenlayer（input）（hidelayer）andoutputlayer（outputlayer）.

2.1BPalgorithm

Theerrorback-propagationalgorithm（BPalgorithm）ofthelearningprocess,bythereverseforwardpropagationanderrorinformationtransmissionconsistsoftwoprocesses.Inputlayerneuro