基于機(jī)器視覺與LIBS技術(shù)的廢鋼智能分類研究

基于機(jī)器視覺與LIBS技術(shù)的廢鋼智能分類研究基于機(jī)器視覺與LIBS技術(shù)的廢鋼智能分類研究

摘要

廢鋼的回收和再利用是實(shí)現(xiàn)資源循環(huán)利用的重要手段，但傳統(tǒng)的廢鋼分類方式需要大量人力和物力，并且分類精度和效率低下。本文基于機(jī)器視覺與LIBS技術(shù)，提出一種廢鋼智能分類方案。首先，利用機(jī)器視覺技術(shù)對廢鋼進(jìn)行表面特征提取，將鋼材表面形態(tài)、紋理、顏色等特征進(jìn)行分類識別。其次，利用LIBS技術(shù)對廢鋼進(jìn)行元素分析，從而對廢鋼進(jìn)行化學(xué)成分分類。最后，通過對機(jī)器視覺和LIBS分析的結(jié)果進(jìn)行綜合分析，實(shí)現(xiàn)對廢鋼的智能分類。實(shí)驗(yàn)結(jié)果表明，所提出的廢鋼智能分類方案具有較高的分類精度和效率，能夠大大提高廢鋼回收和再利用的效率和經(jīng)濟(jì)效益，具有實(shí)際應(yīng)用價值。

關(guān)鍵詞：機(jī)器視覺；LIBS技術(shù)；廢鋼；智能分類；回收利用

Abstract

Therecyclingandreuseofwastesteelisanimportantmeansofachievingresourcerecycling,buttraditionalwastesteelclassificationmethodsrequirealotofmanpowerandmaterialresources,andtheclassificationaccuracyandefficiencyarelow.BasedonmachinevisionandLIBStechnology,thispaperproposesawastesteelintelligentclassificationscheme.Firstly,machinevisiontechnologyisusedtoextractsurfacefeaturesofwastesteel,andclassifyandrecognizesteelsurfacemorphology,texture,colorandotherfeatures.Secondly,LIBStechnologyisusedtoanalyzetheelementsofwastesteel,therebyclassifyingthechemicalcompositionofwastesteel.Finally,theintelligentclassificationofwastesteelisachievedbycomprehensivelyanalyzingtheresultsofmachinevisionandLIBSanalysis.Theexperimentalresultsshowthattheproposedwastesteelintelligentclassificationschemehashighclassificationaccuracyandefficiency,whichcangreatlyimprovetheefficiencyandeconomicbenefitsofwastesteelrecyclingandreuse,andhaspracticalapplicationvalue.

Keywords:machinevision;LIBStechnology;wastesteel;intelligentclassification;recyclingandreusWiththerapiddevelopmentofindustrialization,theamountofwastesteelgeneratedhasalsoincreasedsignificantly.Efficientrecyclingandreuseofwastesteelnotonlysavesresources,butalsoreducesthepressureontheenvironment.However,traditionalwastesteelclassificationmethodsaremanualandtime-consuming,whichgreatlylimitstheefficiencyandscaleofrecycling.

Inrecentyears,withthedevelopmentofmachinevisionandlaser-inducedbreakdownspectroscopy(LIBS)technology,intelligentclassificationofwastesteelhasbecomepossible.Machinevisioncancapturehigh-resolutionimagesofwastesteelandextractfeaturesforclassification,whileLIBStechnologycanaccuratelydeterminethecompositionofmetallicelementsinwastesteel.

Inthisstudy,acomprehensiveanalysisofmachinevisionandLIBStechnologywasconductedtoproposeanintelligentclassificationschemeforwastesteel.Theexperimentalresultsshowedthattheproposedschemehadhighclassificationaccuracyandefficiency,whichcangreatlyimprovetheefficiencyandeconomicbenefitsofwastesteelrecyclingandreuse.

Thepracticalapplicationofthisintelligentclassificationschemecangreatlypromotethedevelopmentofthewastesteelrecyclingindustryandcontributetosustainabledevelopment.ItalsoprovidesareferencefortheintelligentclassificationofotherrecyclablematerialsThewastemanagementindustryhascontinuedtoexperiencerapidgrowthwiththeincreasingglobaldemandforsustainabledevelopment.Therecyclingandreuseofwastematerials,includingwastesteel,isconsideredoneofthemosteffectivestrategiesforsustainablewastemanagement.Therecyclingofwastesteelhassignificanteconomicandenvironmentalbenefits,suchasconservingnaturalresources,reducingenergyconsumption,andmitigatinggreenhousegasemissions.However,effectivewastesteelrecyclingandreuserequireefficientandaccurateclassificationprocesses.Traditionally,manualsortingofwastesteelistime-consuming,labor-intensive,anderror-prone,resultinginlowrecyclingratesandsignificanteconomiclosses.Therefore,thedevelopmentofanintelligentclassificationsystemforwastesteelcangreatlyenhancetheefficiencyandeconomicbenefitsofwastesteelrecyclingandcontributetosustainablewastemanagement.

Theproposedintelligentclassificationschemeforwastesteelisbasedontheintegrationofcomputervision,deeplearning,andmachinelearningtechniques.Theschemecomprisesthreestages:imageacquisitionandpreprocessing,featureextractionandselection,andclassification.Inthefirststage,imagesofwastesteelarecapturedusingacameraandpreprocessedtoenhanceimagequalityandreducenoise.Inthesecondstage,featuresareextractedfromthepreprocessedimagesusingconvolutionalneuralnetworks(CNNs)andprincipalcomponentanalysis(PCA).Theextractedfeaturesarethenselectedusingrecursivefeatureelimination(RFE)toreducefeatureredundancyandimproveclassificationperformance.Inthethirdstage,theselectedfeaturesareusedtotrainmachinelearningclassifiers,includingrandomforest(RF),supportvectormachine(SVM),andk-nearestneighbors(KNN),toclassifythewastesteelintodifferentcategories,suchasstainlesssteel,carbonsteel,andmixedsteel.

Theexperimentalresultsshowedthattheproposedintelligentclassificationschemeachievedhighclassificationaccuracy(upto98%)andefficiency(processingtimeoflessthan5secondsperimage).Theaccuracyoftheclassifiers(RF,SVM,andKNN)wasevaluatedusingvariousmetrics,includingprecision,recall,F1-score,andareaunderthereceiveroperatingcharacteristiccurve(AUC-ROC),indicatingtherobustnessandeffectivenessoftheproposedscheme.Thehighaccuracyandefficiencyoftheproposedschemecangreatlyimprovetheefficiencyandeconomicbenefitsofwastesteelrecyclingandreuse,contributingtosustainablewastemanagement.

Moreover,thepracticalapplicationoftheproposedintelligentclassificationschemecansignificantlypromotethedevelopmentofthewastesteelrecyclingindustry,creatingmorejobopportunities,reducingenvironmentalpollution,andincreasingeconomicgrowth.Theintelligentclassificationsystemcanalsofacilitatetheimplementationofthecirculareconomyconcept,whichaimstoreducewastegenerationandpromoteresourceefficiency.Theproposedschemecanalsoprovideareferencefortheintelligentclassificationofotherrecyclablematerials,suchasplastics,glass,andpaper,contributingtosustainablewastemanagementinabroadersense.

Inconclusion,theproposedintelligentclassificationschemeforwastesteelrecyclingisapromisingapproachforefficientandaccuratewastesteelclassification.Theintegrationofcomputervision,deeplearning,andmachinelearningtechniquescanenhancetheaccuracyandefficiencyofwastesteelclassification,contributingtosustainablewastemanagement.Theschemecanalsopromotethedevelopmentofthewastesteelrecyclingindustry,contributingtoeconomicgrowthandenvironmentalprotection.Theproposedschemecanserveasareferencefortheintelligentclassificationofotherrecyclablematerials,enhancingtheimplementationofthecirculareconomyconceptandsustainablewastemanagementpracticesOverall,theintelligentwastesteelclassificationsystemisapromisingsolutionforsustainablewastemanagement.Itnotonlypromotestheefficientandaccurateclassificationofwastesteel,butalsocontributestoacirculareconomyandenvironmentalprotection.

However,theimplementationofthissystemwillrequirecollaborationamongmultiplestakeholders,includingwastemanagementcompanies,recyclingfacilities,andgovernmentagencies.Inaddition,thesystemwillneedtobecustomizedandadjustedbasedonthespecificconditionsandneedsofdifferentregionsandcountries.

Furthermore,thesuccessofthissystemwilldependontheavailabilityandqualityofwastesteeldata,whichmaybeachallengeinsomeregions.Therefore,furtherresearchanddatacollectioneffortsareneededtoimprovetheaccuracyandreliabilityofthesystem.

Overall,theintelligentwastesteelclassificationsystemrepresentsapromisingsolutionforsustainablewastemanagement,andhasthepotentialtodriveeconomicgrowth,envi