基于BP神经网络的车型识别外文翻译Word文档格式.docx
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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.
(a)Typicallayout
(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.
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”thecontributionfromthepreviousdescentiterations.Thisalgorithmadoptedtopology625-35-NasshowninFig.7.Thesizeofinputvalueis625(25*25)andinitialweightsarewithrandomvalues,desiredoutputvalueshavethesamefeaturewiththeinputvalues.
Fig.7Thenetworktopology
AsFig.7shows,thereisathree-l