车辆图像预处理和车牌定位的方法研究 中英文对照翻译Word文档格式.docx

车辆图像预处理和车牌定位的方法研究 中英文对照翻译Word文档格式.docx

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Medianfiltering;

Binaryzation;

Edgedetection;

Licenseplatelocation

I.INTRODUCTION

Vehiclelicenseplaterecognitionsystembasedonvehiclelicenseforthespecifictargetisdedicatedcomputervisionsystem[1].Itisoneoftheimportantresearchtopicsaboutcomputervisionandpatternrecognitiontechnologyinthefieldofintelligenttransportationapplications.Vehiclelicenseidentificationisthegeneralcomposedbythefollowingprocess:

imageacquisition,imagepreprocessing,licenseplatelocation,charactersegmentation,characterrecognition[2].Thecorrectrateofthelastprocesshasadirectimpactonthenextprocess.Sincetheoriginalimagefromtheacquisitioncardincludesthevehiclelicenseplate,thecaritself,andautomotivebackgroundimage,itisnecessarytoremovethesenon-licensedimagesinordertoextractthecorrectregionallicenseandforthefoundationofthelicenseplatecharacterrecognition.Intheactualsystem,duetonaturalchangesindayandnightillumination,vehicleownmovement,thecameraangleofobservation,collectingimagesoftheequipmentitselfandotherfactorsinfluence,theimageobtainedisnotalwaysverysatisfactory,thereisawiderangeofnoise.Therefore,itisnecessarytomakethelicenseplateimagepre-processingforimprovingimagequality,layingfoundationforthesubsequentlicenseplaterecognition.

II.IMAGEPREPROCESSING

Imagepreprocessingisanessentialprocessinlicenseplaterecognitionsystem,andthequalityofpreprocessingdirectlyaffectsthelocation.Theimagepreprocessinginthisarticleincludesimagegrey,edgedetection,medianfilterandbinaryzation.Thefollowinggivesthedetailstatementonthepreprocessingprocess.

A.Imagegreying

Allvehicleimagesacquiredthroughcameraandimagecardarecolorimage,andimageformatisnotsame.ThecommonlyusedimageformsareJPEGandBMP.Iftreatmentwithacquiredimagedirectly,notonlytheimageformatiscomplex,moreoverthecomputationdataquantityisextremelyhuge,suchlicenseplatelocationcannotsatisfytherequestforfastandreal-time.Therefore,thecolorimageneedtobeformattedprocessing,transformingtheJPEGimageortheBMPimagetoDIB（DeviceIndependentBitmap）whichfavorsthecomputertoprocess.Then,usingR,GandBtricolorweightedaveragemethodprocesstheDIBimage,processingfunctionisshownasequation

（1）:

F（x,y）=0.299*R（x,y）+0.587*G（x,y）+0.114*B（x,y）

（1）

R（x,y）,G（x,y）andB（x,y）areR,GandBtricolorcomponentoftheinputcolorimage[3].Thecolorimagetransformstogreyimagebyequation

（1）processing,theresultisshowninFig.1.

（a）Originalimage

（b）Greyimage

Figure1.Acontrastbetweenoriginalimageandgreyimage

B.Edgedetection

Edgeisthemostbasicfeatureoftheimage,sotheedgeindicatesstepchangeofthegreylevelonitssurroundingpixels.Chinesevehicleslicenseplateregionhasbigcolorcontrastbetweenlicenseplatebottomandlicenseplatecharacter.Thelicenseplateiscomposedof7characterswithrichedgeinformationinturnwhicharetheChinesecharacter,theletterandtheArabicnumeral,andthecharacterinlicenseplateregionandbackgroundhaveobviousedgeintheentirepicture,alsohavemanyedge.Thisisoneofthebasiccharacteristicsthatlicenseplateregiondistinguishesfromotherregioninthevehiclespicture,anditisalsothefundamentalbasisofthisalgorithm.CommonlyusededgedetectionoperatorshavePrewittoperator,Sobeloperator,Cannyoperator,LOGoperator,Robertsoperatorandotheroperator.PrewittoperatorandSobeloperatorarefirst-orderdifferentialoperator,theformeristheaveragefilter,thelatteristheweightedaveragefilter，whiletheimageedgedetectedbythetwomethodsmaybebetterthantwopixels.TheCannymethodusesfirstderivativeasthefoundationtojudgeedgepoints.Itisoneofthebesttraditionalfirst-orderdifferentialoperatorsinthedetectionofstepedge.Theshortcomingissmoothingoutsomedetails[4].LOGoperatorusesGaussianfunctiontosmoothimagefirst,thenusesLaplacetransformtoprocessimage,andthismethodprocessingimageedgeisinsufficientlyclearandthespeedneedstobeimproved.ThelocalizationusingRobertsoperatorisquiteprecise,butmoresensitivetonoise.TheexperimentindicatedthatusingthePrewittedgedetectionoperatorcanbetterstandouttheedgecharacteristicoflicenseplate,andspeedisfaster.Fig.2isseveralimagesafterprocessbydifferentedgedetectionoperator.

（a）Prewittoperator

（b）Sobeloperator

（c）Cannyoperator

（d）LOGoperator

（e）Robertoperator

Figure2.Imagecontrastafterprocessbyseveraledgedetectionoperator

C.Medianfiltering

Medianfilteringmethodisanon-linearsmoothingtechnique.Itsetsthegreylevelofeachpixeltothemiddlevalueofallpixels’greylevelinaneighborhoodwindow[5].Medianfilteringmethodisanon-lineartechniquethatbasedonasequencingstatistictheory.Itcaninhibitthenoiseeffectively.Thebasicprincipleofmedianfilteringistoreplacethevalueofpointindigitalimageornumericalsequenceswiththemiddlevalueofthispoint’soneneighborhood,soitcanletthearoundpixelvalueclosetothispoint’svalue.Thustheisolatednoiseiseliminated.Thismethodutilizesthetwo-dimensionalslidingtemplateofacertainstructure,arrangesthepixelintemplateaccordingtothesizeofpixelvalue,thenarise（ordrop）two-dimensionaldataarraywasproduced.Theoutputoftwo-dimensionalmedianfilteringresultprovidedbyequation

（2）:

G（x,y）=med{F（x-k,y-l）}

（2）

F（x,y）,G（x,y）isrespectivelyfororiginalimageandtheimageafterdealingwith.Wisatwo-dimensionaltemplate.TheresultisshowninFig.3.

Figure3.Medianfilterimage

D.Imagebinaryzation

Imagebinaryzationprocessingisthatsettinggrayvalueofpixelsonimageto0or255,thatis,theentireimagepresentstangibleblackandwhiteeffect[6].Weobtainthebinaryzationimagethroughselecting256brightnesslevelofgreyimagebysuitablethreshold.Binaryzationimagecanstillreflectthewholeandpartialcharacteristicofimage.Indigitalimageprocessing,binaryzationimageholdstheextremelyimportantstatus.First,binaryzationreducestheamountofimagedata.Secondly,tohighlighttheoutlineoftheinterestgoal,thisisfavortofurtherprocessing.Fig.4istheimageafterbinaryzationprocessing.

Figure4.Imageafterbinaryzationprocessing

III.LICENSEPLATELOCATION

Thetaskoflicenseplatelocationistoremovemostunwantedbackgroundinformationfromthewholeimageandfindthelicenseplateregionwithasmallamountofredundantbackground.Becausethelicenseplateregioncontraststothebackground,thehistogramoflicenseplateimageshowsabimodalshapeafterimagepreprocessing.Thewavetroughbetweentwowavepeakscorrespondingtothegraylevelisselectedasathreshold.SupposingtheimageisdividedbyF（x,y）andthegraylevelrangeis[Z1,Zk].Fig.5showstherearetwoobviouswavepeaksingraylevelZiandZj,andinZtthereisawavetrough.BychoosingZtreasonably,B1beltcancontaingreylevelcorrelationtothebackgroundasfaraspossible,whiletheB2bandincludesgreylevelcorrelationtothelicenseplateasfaraspossible[7].

Figure5.Doublepeakofhistogram

A.Locatingupperandlowerboundary

Onecharacteristicoflicenseplateimageisthecrowdedcharactersintheinternal,sothegreyjumpisextremelyfierce.Wefindthepossiblelocationoflicenseplateregionbyusingrowgreyjumpruleofgreyimage.Wepreservethispositionandcallitasthefakelicenseplateregion.Specificalgorithmincludingfollowingsteps:

Step1：

Calculatingthelevelhistogramofimage,andsmoothingthelevelhistogramwith[1,1,1,1,1]/5operator.

Step2：

Searchingthebottomedgedistanceoflicenseplatefromtheimagebase,if5linewhichispredefinedas5hassatisfiedtherequestcontinuouslywhichthevalueisbiggerthanorequalto10pixelsinhistogram,andthevalueofcurrentlinediffersabove4pixelswiththevalueoffronttheNthline,thenwebelievethatthebottomedgedistanceoflicenseplatehasfounded.Currentlineminus5,andlocatesthescanlinetothesummitofcurrentpeak.Ifthecurrentlinedoesnotsatisfythecondition,thencontinuestosearchupwardlyuntilthetopmarginofimage.

Step3：

Locatingthecurrentlinetothebottomofupwavecrest,ifthepeakbottomvalueisgreaterthanthemaximumvalue,thenlocatingtothesummitofcurrentpeak,andthesummitformaximumvalueline;

searchingupwardlyfromthecurrentline’snextline,ifthevalueofsearchlineisgreaterthantherecordedmaximumvalue,thensettingthecurrentlineasmaximumvalueandcarryingonsearchingupwardlyfromit.Otherwise,ifthecurrentvalueissmallerthantwo-thirdsofmaximumvalue,orthecurrentvalueis