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一种基于图像处理的轮胎X光图像缺陷检测方法.docx

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一种基于图像处理的轮胎X光图像缺陷检测方法 Title:AnImageProcessing-basedApproachforTireX-rayImageDefectDetection Abstract: Tiremanufacturingqualitycontrolisofparamountimportancetoensuresafetyandperformance.Thispaperproposesaninnovativeimageprocessing-basedapproachfordetectingdefectsintireX-rayimages.Themethodinvolvesaseriesofpre-processingsteps,featureextraction,anddefectclassificationusingmachinelearningtechniques.Theproposedapproachshowspromisingresultsinaccuratelyidentifyingvarioustypesoftiredefectswithhighprecision. Introduction: Thetireindustryplaysacriticalroleinensuringvehiclesafetyandperformance.Defectivetirescanleadtoaccidentsandinjuries,makingtirequalitycontrolessential.Traditionalmethodsfortiredefectdetectionoftenrelyonmanualinspection,whichistime-consuming,subjective,andpronetohumanerrors.Inrecentyears,variousautomatedmethods,especiallythosebasedonimageprocessingtechniques,havegainedpopularityduetotheirefficiencyandaccuracy. Methodology: Theproposedmethodconsistsofseveralsteps:imageacquisition,pre-processing,featureextraction,anddefectclassification. ImageAcquisition: TireX-rayimagesareacquiredusingspecializedX-raymachines,allowinginternaltirestructurestobevisible.Theacquiredimagesprovideabasisforsubsequentanalysisanddefectdetection. Pre-processing: Toenhancethequalityandclarityoftheimages,pre-processingtechniquesareemployed.Thesetechniquesincludenoisereduction,contrastenhancement,andimageenhancementalgorithmstooptimizetheimagesfordefectdetection. FeatureExtraction: Extractingeffectiveanddiscriminativefeaturesfromthepre-processedimagesiscrucialforaccuratedefectdetection.Varioustechniquescanbeemployed,suchastextureanalysis,edgedetection,andshape-basedanalysis.Thefeaturesextractedfromtheimagesprovidecrucialinformationaboutthetire'sstructuralpropertiesandpotentialdefects. DefectClassification: Oncetherelevantfeaturesareextracted,amachinelearningalgorithmisappliedtoclassifythedefects.Supervisedlearningalgorithms,suchassupportvectormachines(SVM),randomforests,orartificialneuralne