Automatic Localization of Casting Defects withConvolutional Neural Networks 鑄件缺陷的自動定位 卷積神經(jīng)網(wǎng)絡(luò)

Automatic Localization of Casting Defects withConvolutional Neural Networks 鑄件缺陷的自動定位 卷積神經(jīng)網(wǎng)絡(luò)

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時間：2019-08-08

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1、AutomaticLocalizationofCastingDefectswithConvolutionalNeuralNetworksMaxFergusonRonayAkYung-TsunTinaLeeKinchoH.LawEngineeringInformaticsGroupSystemsIntegrationDivisionSystemsIntegrationDivisionEngineeringInformaticsGroupCivilandEnvironmentalNationalInsti

2、tuteofStandardsNationalInstituteofStandardsCivilandEnvironmentalEngineeringandTechnology(NIST)andTechnology(NIST)EngineeringStanfordUniversityGaithersburg,UnitedStatesGaithersburg,UnitedStatesStanfordUniversityStanford,UnitedStatesronay.ak@nist.govyung-

3、tsun.lee@nist.govStanford,UnitedStatesmaxferg@stanford.edulaw@stanford.eduAbstract—AutomaticlocalizationofdefectsinmetalcastingsisThereareanumberofnondestructiveexamination(NDE)achallengingtask,owingtotherareoccurrenceandvariationintechniquesavailablefo

4、rproducingtwo-dimensionalandthree-appearanceofdefects.Convolutionalneuralnetworks(CNN)havedimensionalimagesofanobject.Real-timeX-rayimagingrecentlyshownoutstandingperformanceinbothimagetechnologyiswidelyusedindefectdetectionsystemsinclassificationandloc

5、alizationtasks.Weexaminehowseveralindustry,suchason-linewelddefectinspection[3].UltrasonicdifferentCNNarchitecturescanbeusedtolocalizecastingdefectsinspectionandmagneticparticleinspectioncanalsobeusedtoinX-rayimages.Wetakeadvantageoftransferlearningtoal

6、lowmeasurethesizeandpositionofcastingdefectsincaststate-of-the-artCNNlocalizationmodelstobetrainedonacomponents[4,5].Analternativemethodisthree-dimensionalrelativelysmalldataset.Inanalternativeapproach,wetrainaX-raycomputedtomography,thatcanbeusedtovisu

7、alizethedefectclassificationmodelonaseriesofdefectimagesandtheninternalstructureofmaterials.RecentdevelopmentsinhighuseaslidingclassifiermethodtodevelopasimplelocalizationresolutionX-raycomputedtomographyhavemadeitpossibletomodel.Wecomparethelocalizatio

8、naccuracyandcomputationalgainathree-dimensionalcharacterizationofporosity[6,7].performanceofeachtechnique.WeshowpromisingresultsfordefectlocalizationontheGRIMAdatabaseofX-rayimagesThedefectdetectionprocesscanbeframedaseitheran(GD

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