车牌识别外文文献翻译中英文.docx

车牌识别外文文献翻译中英文.docx

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外文文献翻译

（含：

英文原文及中文译文）

文献出处：

GaoQ,WangX,XieG.LicensePlateRecognitionBasedOnPriorKnowledge[C]//IEEEInternationalConferenceonAutomationandLogistics.IEEE,2007:

2964-2968.

英文原文

LicensePlateRecognitionBasedOnPriorKnowledge

QianGao,XinnianWangandGongfuXie

Abstract-Inthispaper,anewalgorithmbasedonimprovedBP（backpropagation）neuralnetworkforChinesevehiclelicenseplaterecognition（LPR）isdescribed.Theproposedapproachprovidesasolutionforthevehiclelicenseplates（VLP）whichweredegradedseverely.Whatitremarkablydiffersfromthetraditionalmethodsistheapplicationofpriorknowledgeoflicenseplatetotheprocedureoflocation,segmentationandrecognition.Colorcollocationisusedtolocatethelicenseplateintheimage.Dimensionsofeachcharacterareconstant,whichisusedtosegmentthecharacterofVLPs.TheLayoutoftheChineseVLPisanimportantfeature,whichisusedtoconstructaclassifierforrecognizing.Theexperimentalresultsshowthattheimprovedalgorithmiseffectiveundertheconditionthatthelicenseplatesweredegradedseverely.

IndexTerms-Licenseplaterecognition,priorknowledge,vehiclelicenseplates,neuralnetwork.

I. INTRODUCTION

VehicleLicense-Plate（VLP）recognitionisaveryinterestingbutdifficultproblem.Itisimportantinanumberofapplicationssuchasweight-and-speed-limit,redtrafficinfringement,roadsurveysandparksecurity[1].VLPrecognitionsystemconsistsoftheplatelocation,thecharacterssegmentation,andthecharactersrecognition.Thesetasksbecomemoresophisticatedwhendealingwithplateimagestakeninvariousinclinedanglesorundervariouslighting,weatherconditionandcleanlinessoftheplate.Becausethisproblemisusuallyusedinreal-timesystems,itrequiresnotonlyaccuracybutalsofastprocessing.MostexistingVLPrecognitionmethods[2],[3],[4],[5]reducethecomplexityandincreasetherecognitionratebyusingsomespecificfeaturesoflocalVLPsandestablishingsomeconstrainsontheposition,distancefromthecameratovehicles,andtheinclinedangles.Inaddition,neuralnetworkwasusedtoincreasetherecognitionrate[6],[7]butthetraditionalrecognitionmethodsseldomconsiderthepriorknowledgeofthelocalVLPs.Inthispaper,weproposedanewimprovedlearningmethodofBPalgorithmbasedonspecificfeaturesofChineseVLPs.TheproposedalgorithmovercomesthelowspeedconvergenceofBPneuralnetwork[8]andremarkableincreasestherecognitionrateespeciallyundertheconditionthatthelicenseplateimagesweredegradeseverely.

II. SPECIFICFEATURESOFCHINESEVLPS

A.Dimensions

Accordingtotheguidelineforvehicleinspection[9],alllicenseplatesmustberectangularandhavethedimensionsandhaveall7characterswritteninasingleline.Underpracticalenvironments,thedistancefromthecameratovehiclesandtheinclinedanglesareconstant,soallcharactersofthelicenseplatehaveafixedwidth,andthedistancebetweenthemediumaxesoftwoadjoiningcharactersisfixedandtheratiobetweenwidthandheightisnearlyconstant.Thosefeaturescanbeusedtolocatetheplateandsegmenttheindividualcharacter.B.Colorcollocationoftheplate

TherearefourkindsofcolorcollocationfortheChinesevehiclelicenseplate.ThesecolorcollocationsareshownintableI.

TABLEI

Moreover,militaryvehicleandpolicewagonplatescontainaredcharacterwhichbelongstoaspecificcharacterset.Thisfeaturecanbeusedtoimprovetherecognitionrate.

C.LayoutoftheChineseVLPS

ThecriterionofthevehiclelicenseplatedefinesthecharacterslayoutofChineselicenseplate.AllstandardlicenseplatescontainChinesecharacters,numbersandletterswhichareshowninFig.l.ThefirstoneisaChinesecharacterwhichisanabbreviationofChinese

provinces.ThesecondoneisaletterrangingfromAtoZexcepttheletterI.Thethirdandfourthonesarelettersornumbers.Thefifthtoseventhonesarenumbersrangingfrom0to9only.Howeverthefirstortheseventhonesmayberedcharactersinspecialplates（asshowninFig.l）.Aftersegmentationprocesstheindividualcharacterisextracted.Takingadvantageofthelayoutandcolorcollocationpriorknowledge,theindividualcharacterwillenteroneoftheclasses:

abbreviationsofChineseprovincesset,lettersset,lettersornumbersset,numberset,specialcharactersset.

（a） Typicallayout

（b） Specialcharacter

Fig.lThelayoutoftheChineselicenseplate

III. THEPROPOSEDALGORITHM

Thisalgorithmconsistsoffourmodules:

VLPlocation,charactersegmentation,characterclassificationandcharacterrecognition.ThemainstepsoftheflowchartofLPRsystemareshowninFig.2.

Firstlythelicenseplateislocatedinaninputimageandcharactersaresegmented.Theneveryindividualcharacterimageenterstheclassifiertodecidewhichclassitbelongsto,andfinallytheBPnetworkdecideswhichcharacterthecharacterimagerepresents.

A.Preprocessingthelicenseplate

1）VLPLocation

Thisprocesssufficientlyutilizesthecolorfeaturesuchascolorcollocation,colorcentersanddistributionintheplateregion,whicharedescribedinsectionII.Thesecolorfeaturescanbeusedtoeliminatethedisturbanceofthefakeplate'sregions.TheflowchartoftheplatelocationisshowninFig.3.

Fig.3Theflowchartoftheplatelocationalgorithm

Theregionswhichstructureandtexturesimilartothevehicleplateareextracted.Theprocessisdescribedasfollowed:

Here,theGaussianvarianceissettobelessthanW/3（Wisthecharacterstrokewidth）,soIPgetsitsmaximumvalueMatthecenterofthestroke.Afterconvolution,binarizationisperformedaccordingtoathresholdwhichequalsT*M（T<0.5）,Medianfilterisusedtopreservetheedgegradientandeliminateisolatednoiseofthebinaryimage.AnN*Nrectanglemedianfilterisset,andNrepresentstheoddintegermostlyclosetoW.

Morphologyclosingoperationcanbeusedtoextractthecandidateregion.Theconfidencedegreeofcandidateregionforbeingalicenseplateisverifiedaccordingtotheaspectratioandareas.Here,theaspectratioissetbetween1.5and4forthereasonofinclination.Thepriorknowledgeofcolorcollocationisusedtolocateplateregionexactly.ThelocatingprocessofthelicenseplateisshowninFig.4.

2）Charactersegmentation

Thispartpresentsanalgorithmforcharactersegmentationbasedonpriorknowledge,usingcharacterwidth,fixednumberofcharacters,theratioofheighttowidthofacharacter,andsoon.TheflowchartofthecharactersegmentationisshowninFig.5.

Firstly,preprocessthelicensetheplateimage,suchasunevenilluminationcorrection,contrastenhancement,inclinecorrectionandedgeenhancementoperations;secondly,eliminatingspacemarkwhichappearsbetweenthesecondcharacterandthethirdcharacter;thirdly,mergingthesegmentedfragmentsofthecharacters.InChina,allstandardlicenseplatescontainonly7characters（seeFig.1）.Ifthenumberofsegmentedcharactersislargerthanseven,themergingprocessmustbeperformed.TableIIshowsthemergingprocess.Finally,extractingtheindividualcharacter'imagebasedonthenumberandthewidthofthecharacter.Fig.6showsthesegmentationresults,（a）Theinclineandbrokenplateimage,（b）theinclineanddistortplateimage,（c）theseriousfadeplateimage,（d）thesmutlicenseplateimage.

whereNfisthenumberofcharactersegments,MaxFisthenumberofthelicenseplate,andiistheindexofeachcharactersegment.

Themediumpointofeachsegmentedcharacterisdeterminedby:

（3）

whereliS

istheinitialcoordinatesforthecharactersegment,and2iSisthefinalcoordinateforthecharactersegment.Thedistancebetweentwoconsecutivemediumpointsiscalculatedby:

（4）

Fig.6Thesegmentationresults

B.Usingspecificpriorknowledgeforrecognition

ThelayoutoftheChineseVLPisanimportantfeature（asdescribedinthesectionII）,whichcanbeusedtoconstructaclassifierforrecognizing.Therecognizingprocedureadoptedconjugategradientdescentfastlearningmethod,whichisanimprovedlearningmethodofBPneuralnetwork[10].Conjugategradientdescent,whichemploysaseriesoflinesearchesinweightorparameterspace.Onepicksthefirstdescentdirectionandmovesalongthatdirectionuntiltheminimuminerrorisreached.Theseconddescentdirectionisthencomputed:

thisdirectionthe''conjugatedirection"istheonealongwhichthegradientdoesnotchangeitsdirectionwillnot''spoil"thecontributionfromthepreviousdescentiterations.Thisalgorithmadoptedtopology625-35-NasshowninFig.7.Thesizeofinputvalueis625（25*25）andinitialweightsarewithrandomvalues,desiredoutputvalueshavethesamefeaturewiththeinputvalues.

AsFig.7shows,thereisathree-layernetworkwhichcontainsworkingsignalfeedforwardoperationandreversepropagationoferrorprocesses.Thetargetparameteristandthelengthofnetworkoutputvectorsisn.Sigmoidisthenonlineartransferfunction,weightsareinitializedwithrandomvalues,andchangedinadirectionthatwillreducetheerrors.

Thealgorithmwastrainedwith1000imagesofdifferentbackgroundandilluminationmostofwhichweredegradeseverely.Afterpreprocessingprocess,theindividualcharactersarestored.Allcharactersusedfortrainingandtestinghavethesamesize（25*25）.Theintegratedprocessforlicenseplaterecognitionconsistsofthefollowingsteps:

1） Featureextracting

Thefeaturevectorsfromseparatedcharacterimageshavedirecteffectsontherecognitionrate.Manymethodscanbeusedtoextractfeatureoftheimagesamples,e.g.statisticsofdataatvertica