车牌识别外文翻译文献综述.docx
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车牌识别外文翻译文献综述.docx
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车牌识别外文翻译文献综述
车牌识别外文翻译文献综述
(文档含中英文对照即英文原文和中文翻译)
LicensePlateRecognitionBasedOnPriorKnowledge
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
Categoryoflicenseplate
Colorcollocation
smallhorsepowerplate
bluebackgroundandwhitecharacters
motortruckplate
yellowbackgroundandblackcharacters
militaryvehicleandpolicewagonplate
blackbackgroundandthewhitecharacters
embassyvehicleplate
whitebackgroundandblackcharacters
Moreover,militaryvehicleandpolicewagonplatescontainaredcharacterwhichbelongstoaspecificcharacterset.Thisfeaturecanbeusedtoimprovetherecognitionrate.
C.LayoutoftheChineseVLPS
ThecriterionofthevehiclelicenseplatedefinesthecharacterslayoutofChineselicenseplate.AllstandardlicenseplatescontainChinesecharacters,numbersandletterswhichareshowninFig.1.ThefirstoneisaChinesecharacterwhichisanabbreviationofChineseprovinces.ThesecondoneisaletterrangingfromAtoZexcepttheletterI.Thethirdandfourthonesarelettersornumbers.Thefifthtoseventhonesarenumbersrangingfrom0to9only.Howeverthefirstortheseventhonesmayberedcharactersinspecialplates(asshowninFig.1).Aftersegmentationprocesstheindividualcharacterisextracted.Takingadvantageofthelayoutandcolorcollocationpriorknowledge,theindividualcharacterwillenteroneoftheclasses:
abbreviationsofChineseprovincesset,lettersset,lettersornumbersset,numberset,specialcharactersset.
辽BA9083
Chinese
character
character
Letter
Letteror
number
Number
(a)Typicallayout
辽BB092警
Chinese
character
character
Letter
Letteror
number
Specialred
character
(b)Specialcharacter
Fig.1ThelayoutoftheChineselicenseplate
III.THEPROPOSEDALGORITHM
Thisalgorithmconsistsoffourmodules:
VLPlocation,charactersegmentation,characterclassificationandcharacterrecognition.ThemainstepsoftheflowchartofLPRsystemareshowninFig.2.
Firstlythelicenseplateislocatedinaninputimageandcharactersaresegmented.Theneveryindividualcharacterimageenterstheclassifiertodecidewhichclassitbelongsto,andfinallytheBPnetworkdecideswhichcharacterthecharacterimagerepresents.
Imageacquisition
Platelocation
Characterssegmentationsegmentation
classifier
Chinese
character
Letter
Letteror
number
Number
Specialcharacter
Charactersrecognition
Fig.2TheflowchartofLPRsystem
A.Preprocessingthelicenseplate
1)VLPLocation
Thisprocesssufficientlyutilizesthecolorfeaturesuchascolorcollocation,colorcentersanddistributionintheplateregion,whicharedescribedinsectionII.Thesecolorfeaturescanbeusedtoeliminatethedisturbanceofthefakeplate’sregions.TheflowchartoftheplatelocationisshowninFig.3.
Charactersedgedetection
Binaryimagesegmenting
Candidateimagedetection
Vehicleplateextraction
Fig.3Theflowchartoftheplatelocationalgorithm
Theregionswhichstructureandtexturesimilartothevehicleplateareextracted.Theprocessisdescribedasfollowed:
(1)
(2)
Here,theGaussianvariance
issettobelessthanW/3(Wisthecharacterstrokewidth),so
getsitsmaximumvalueMatthecenterofthestroke.Afterconvolution,binarizationisperformedaccordingtoathresholdwhichequalsT*M(T<0.5).Medianfilterisusedtopreservetheedgegradientandeliminateisolatednoiseofthebinaryimage.AnN*Nrectanglemedianfilterisset,andNrepresentstheoddintegermostlyclosetoW.
Morphologyclosingoperationcanbeusedtoextractthecandidateregion.Theconfidencedegreeofcandidateregionforbeingalicenseplateisverifiedaccordingtotheaspectratioandareas.Here,theaspectratioissetbetween1.5and4forthereasonofinclination.Thepriorknowledgeofcolorcollocationisusedtolocateplateregionexactly.ThelocatingprocessofthelicenseplateisshowninFig.4.
Fig.4Thewholeprocessoflocatinglicenseplate
2)Charactersegmentation
Thispartpresentsanalgorithmforcharactersegmentationbasedonpriorknowledge,usingcharacterwidth,fixednumberofcharacters,theratioofheighttowidthofacharacter,andsoon.TheflowchartofthecharactersegmentationisshowninFig.5.
Licenseplateimage
preprocessing
Obtainbinaryimage
Verticalprojection
Eliminatespacemark
Fig.5Theflowchartofthecharactersegmentation
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.
TABLEII
GetNf
IfNF>MaxF
Foreachcharactersegments
Calculatethemediumpoint
Foreachtwoconsecutivemediumpoints
Calculatethedistance
Calculatetheminimumdistance
Mergethecharactersegmentkandthecharactersegmentk+1
NF=NF-1
Endofalgorithm
whereNfisthenumberofcharactersegments,MaxFisthenumberofthelicenseplate,andiistheindexofeachcharactersegment.
Themediumpointofeachsegmentedcharacterisdeterminedby:
(3)
where
istheinitialcoordinatesforthecharactersegment,and
isthefinalcoordinateforthecharactersegment.Thedistancebetweentwoconsecutivemediumpointsiscalculatedby:
(4)
Fig.6Thesegmentationresults
B.Usingspecificpriorknowledgeforrecognition
ThelayoutoftheChineseVLPisanimportantfeature(asdescribedinthesectionII),whichcanbeusedtoconstructaclassifierforrecognizing.Therecognizingprocedureadoptedconjugategradientdescentfastlearningmethod,whichisanimprovedlearningmethodofBPneuralnetwork[10].Conjugategradientdescent,whichemploysaseriesoflinesearchesinweightorparameterspace.Onepicksthefirstdescentdirectionandmovesalongthatdirectionuntiltheminimuminerrorisreached.Theseconddescentdirectionisthencomputed:
thisdirectionthe“conjugatedirection”istheonealongwhichthegradientdoesnotchangeitsdirectionwillnot“spoil”thecontributionfromthepreviousdescenti
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