人臉口罩佩戴檢測研究綜述

人臉口罩佩戴檢測研究綜述一、本文概述Overviewofthisarticle隨著和計(jì)算機(jī)視覺技術(shù)的快速發(fā)展，人臉識別技術(shù)已經(jīng)在多個(gè)領(lǐng)域得到了廣泛應(yīng)用。然而，在實(shí)際應(yīng)用中，人臉圖像的質(zhì)量往往受到各種因素的影響，其中最常見的問題就是口罩佩戴?？谡值呐宕鞑粌H會遮擋部分面部特征，還會改變面部的光照、紋理等信息，從而給人臉識別帶來挑戰(zhàn)。因此，研究人臉口罩佩戴檢測技術(shù)具有重要的現(xiàn)實(shí)意義和應(yīng)用價(jià)值。Withtherapiddevelopmentofcomputervisiontechnology,facialrecognitiontechnologyhasbeenwidelyappliedinmultiplefields.However,inpracticalapplications,thequalityoffacialimagesisoftenaffectedbyvariousfactors,amongwhichthemostcommonproblemiswearingmasks.Wearingamasknotonlyobscuressomefacialfeatures,butalsochangesthelighting,texture,andotherinformationoftheface,posingchallengestofacialrecognition.Therefore,studyingfacialmaskwearingdetectiontechnologyhasimportantpracticalsignificanceandapplicationvalue.本文旨在對人臉口罩佩戴檢測研究進(jìn)行全面的綜述和分析。我們將介紹人臉識別技術(shù)的發(fā)展歷程和現(xiàn)狀，以及口罩佩戴對人臉識別的影響。然后，我們將重點(diǎn)回顧和分析近年來人臉口罩佩戴檢測的研究進(jìn)展，包括基于傳統(tǒng)圖像處理的方法和基于深度學(xué)習(xí)的方法。在此基礎(chǔ)上，我們將對各種方法的優(yōu)缺點(diǎn)進(jìn)行比較和討論，并指出未來的研究方向和挑戰(zhàn)。我們還將探討人臉口罩佩戴檢測技術(shù)在實(shí)際應(yīng)用中的潛力和前景。Thisarticleaimstoprovideacomprehensivereviewandanalysisofresearchonfacialmaskwearingdetection.Wewillintroducethedevelopmentandcurrentstatusoffacialrecognitiontechnology,aswellastheimpactofmaskwearingonfacialrecognition.Then,wewillfocusonreviewingandanalyzingtheresearchprogressinfacemaskwearingdetectioninrecentyears,includingmethodsbasedontraditionalimageprocessingandmethodsbasedondeeplearning.Onthisbasis,wewillcompareanddiscusstheadvantagesanddisadvantagesofvariousmethods,andpointoutfutureresearchdirectionsandchallenges.Wewillalsoexplorethepotentialandprospectsoffacialmaskwearingdetectiontechnologyinpracticalapplications.通過本文的綜述和分析，我們希望能夠?yàn)橄嚓P(guān)領(lǐng)域的研究人員提供有價(jià)值的參考和啟示，推動(dòng)人臉口罩佩戴檢測技術(shù)的進(jìn)一步發(fā)展和應(yīng)用。Throughthereviewandanalysisofthisarticle,wehopetoprovidevaluablereferenceandinspirationforresearchersinrelatedfields,andpromotethefurtherdevelopmentandapplicationoffacialmaskwearingdetectiontechnology.二、人臉口罩佩戴檢測方法概述Overviewofdetectionmethodsforwearingfacialmasks隨著和計(jì)算機(jī)視覺技術(shù)的快速發(fā)展，人臉口罩佩戴檢測已經(jīng)成為一個(gè)備受關(guān)注的研究領(lǐng)域。這項(xiàng)技術(shù)的核心在于準(zhǔn)確識別圖像或視頻中人臉的存在與否，以及口罩是否被正確佩戴。近年來，研究者們提出了多種方法來解決這一問題，這些方法大致可以分為基于傳統(tǒng)圖像處理的方法和基于深度學(xué)習(xí)的方法。Withtherapiddevelopmentofcomputervisiontechnology,facialmaskwearingdetectionhasbecomeahighlyfocusedresearchfield.Thecoreofthistechnologyliesinaccuratelyidentifyingthepresenceorabsenceoffacesinimagesorvideos,aswellaswhethermasksareworncorrectly.Inrecentyears,researchershaveproposedvariousmethodstosolvethisproblem,whichcanberoughlydividedintotraditionalimageprocessingbasedmethodsanddeeplearningbasedmethods.基于傳統(tǒng)圖像處理的方法通常依賴于手工設(shè)計(jì)的特征和一系列圖像處理步驟。例如，通過顏色、紋理或形狀等特征來區(qū)分人臉和口罩。然而，這些方法在面對復(fù)雜背景、光照變化或口罩類型多樣性時(shí)，其準(zhǔn)確性和魯棒性往往受到限制。Traditionalimageprocessingmethodstypicallyrelyonmanuallydesignedfeaturesandaseriesofimageprocessingsteps.Forexample,distinguishingbetweenafaceandamaskthroughfeaturessuchascolor,texture,orshape.However,thesemethodsareoftenlimitedintheiraccuracyandrobustnesswhenfacingcomplexbackgrounds,changesinlighting,ordiversemasktypes.近年來，基于深度學(xué)習(xí)的方法在人臉口罩佩戴檢測方面取得了顯著進(jìn)展。深度學(xué)習(xí)模型，特別是卷積神經(jīng)網(wǎng)絡(luò)（CNN），能夠從大量數(shù)據(jù)中自動(dòng)學(xué)習(xí)有用的特征表示。通過訓(xùn)練大規(guī)模標(biāo)注數(shù)據(jù)集，這些模型能夠準(zhǔn)確識別各種人臉和口罩類型，并在不同環(huán)境條件下保持較高的性能。Inrecentyears,deeplearningbasedmethodshavemadesignificantprogressinfacialmaskwearingdetection.Deeplearningmodels,especiallyconvolutionalneuralnetworks(CNNs),canautomaticallylearnusefulfeaturerepresentationsfromlargeamountsofdata.Bytraininglarge-scaleannotateddatasets,thesemodelscanaccuratelyrecognizevarioustypesoffacesandmasks,andmaintainhighperformanceunderdifferentenvironmentalconditions.除了基本的CNN模型外，研究者們還探索了多種改進(jìn)策略，以提高口罩佩戴檢測的準(zhǔn)確性。例如，一些方法利用注意力機(jī)制來強(qiáng)化模型對口罩區(qū)域的關(guān)注；另一些方法則采用多模態(tài)數(shù)據(jù)融合，結(jié)合圖像和視頻信息來增強(qiáng)模型的感知能力。InadditiontothebasicCNNmodel,researchershavealsoexploredvariousimprovementstrategiestoimprovetheaccuracyofmaskwearingdetection.Forexample,somemethodsuseattentionmechanismstostrengthenthemodel'sattentiontothemaskarea;Othermethodsusemultimodaldatafusion,combiningimageandvideoinformationtoenhancethemodel'sperceptualability.隨著對抗生成網(wǎng)絡(luò)（GAN）等生成式模型的發(fā)展，研究者們也開始探索利用這些技術(shù)來合成更多樣化的口罩佩戴人臉數(shù)據(jù)，從而進(jìn)一步提升檢測模型的泛化能力。WiththedevelopmentofgenerativemodelssuchasAdversarialGenerativeNetworks(GANs),researchershavealsobeguntoexploretheuseofthesetechnologiestosynthesizemorediversemaskwearingfacedata,therebyfurtherimprovingthegeneralizationabilityofdetectionmodels.人臉口罩佩戴檢測方法在近年來取得了顯著進(jìn)展，但仍面臨一些挑戰(zhàn)。未來，隨著技術(shù)的不斷進(jìn)步和更多研究工作的深入，我們有理由相信這一領(lǐng)域?qū)⑷〉酶语@著的成果。Thedetectionmethodforwearingfacialmaskshasmadesignificantprogressinrecentyears,butstillfacessomechallenges.Inthefuture,withthecontinuousadvancementoftechnologyandthedeepeningofmoreresearchwork,wehavereasontobelievethatthisfieldwillachievemoresignificantresults.三、各種方法的優(yōu)缺點(diǎn)分析Analysisoftheadvantagesanddisadvantagesofvariousmethods在人臉口罩佩戴檢測的研究中，涌現(xiàn)出了多種方法和技術(shù)。這些方法大致可以分為基于傳統(tǒng)圖像處理的方法、基于深度學(xué)習(xí)的方法和基于混合模型的方法。Intheresearchoffacialmaskwearingdetection,variousmethodsandtechnologieshaveemerged.Thesemethodscanberoughlydividedintotraditionalimageprocessingbasedmethods,deeplearningbasedmethods,andhybridmodelbasedmethods.基于傳統(tǒng)圖像處理的方法主要利用顏色、紋理、形狀等特征來識別口罩佩戴情況。這類方法實(shí)現(xiàn)簡單，計(jì)算量小，但受限于圖像質(zhì)量和光照條件。在復(fù)雜背景下，這些方法可能會受到干擾，導(dǎo)致檢測準(zhǔn)確率下降。Traditionalimageprocessingmethodsmainlyutilizefeaturessuchascolor,texture,andshapetoidentifymaskwearing.Thistypeofmethodissimpletoimplementandrequiressmallcomputationalcomplexity,butislimitedbyimagequalityandlightingconditions.Incomplexbackgrounds,thesemethodsmaybesubjecttointerference,resultinginadecreaseindetectionaccuracy.深度學(xué)習(xí)的方法，尤其是卷積神經(jīng)網(wǎng)絡(luò)（CNN），在人臉口罩佩戴檢測中表現(xiàn)出色。它們能夠從大量數(shù)據(jù)中學(xué)習(xí)復(fù)雜的特征表示，并在各種場景下實(shí)現(xiàn)較高的檢測準(zhǔn)確率。然而，深度學(xué)習(xí)方法的計(jì)算量大，需要高性能的計(jì)算資源，且對數(shù)據(jù)量有一定的要求。模型訓(xùn)練時(shí)間長，且可能存在過擬合的風(fēng)險(xiǎn)。Themethodsofdeeplearning,especiallyConvolutionalNeuralNetworks(CNNs),performwellinfacemaskwearingdetection.Theycanlearncomplexfeaturerepresentationsfromalargeamountofdataandachievehighdetectionaccuracyinvariousscenarios.However,deeplearningmethodsrequirealargeamountofcomputation,requirehigh-performancecomputingresources,andhavecertainrequirementsfordatavolume.Themodeltrainingtimeislongandtheremaybeariskofoverfitting.混合模型結(jié)合了傳統(tǒng)圖像處理方法和深度學(xué)習(xí)方法，旨在充分利用兩者的優(yōu)點(diǎn)。這類方法通常首先使用傳統(tǒng)圖像處理方法進(jìn)行預(yù)處理和特征提取，然后利用深度學(xué)習(xí)模型進(jìn)行分類和檢測?；旌夏Ｐ湍軌蛟谝欢ǔ潭壬咸岣邫z測準(zhǔn)確率，并減少計(jì)算量。如何有效地結(jié)合兩種方法，以及如何在兩者之間找到平衡點(diǎn)，是混合模型方法面臨的主要挑戰(zhàn)。Thehybridmodelcombinestraditionalimageprocessingmethodsanddeeplearningmethods,aimingtofullyutilizetheadvantagesofboth.Thistypeofmethodusuallyfirstusestraditionalimageprocessingmethodsforpreprocessingandfeatureextraction,andthenutilizesdeeplearningmodelsforclassificationanddetection.Hybridmodelscanimprovedetectionaccuracytoacertainextentandreducecomputationalcomplexity.Howtoeffectivelycombinethetwomethodsandfindabalancebetweenthemisthemainchallengefacedbyhybridmodelmethods.各種方法在人臉口罩佩戴檢測中都有其優(yōu)缺點(diǎn)。在實(shí)際應(yīng)用中，需要根據(jù)具體場景和需求選擇合適的方法，并不斷優(yōu)化和改進(jìn)，以提高檢測的準(zhǔn)確性和效率。Variousmethodshavetheiradvantagesanddisadvantagesinfacialmaskwearingdetection.Inpracticalapplications,itisnecessarytochooseappropriatemethodsbasedonspecificscenariosandneeds,andcontinuouslyoptimizeandimprovethemtoimprovetheaccuracyandefficiencyofdetection.四、實(shí)際應(yīng)用案例分析Analysisofpracticalapplicationcases人臉口罩佩戴檢測技術(shù)在現(xiàn)實(shí)生活中有著廣泛的應(yīng)用，尤其在公共衛(wèi)生、安全監(jiān)控、商業(yè)應(yīng)用等領(lǐng)域發(fā)揮著重要作用。以下將詳細(xì)分析幾個(gè)具體的應(yīng)用案例，以展示這項(xiàng)技術(shù)的實(shí)際效能和潛力。Facialmaskwearingdetectiontechnologyhasawiderangeofapplicationsinreallife,especiallyinfieldssuchaspublichealth,safetymonitoring,andcommercialapplications.Thefollowingwillprovideadetailedanalysisofseveralspecificapplicationcasestodemonstratethepracticaleffectivenessandpotentialofthistechnology.在新冠疫情期間，人臉口罩佩戴檢測技術(shù)在公共衛(wèi)生管理中發(fā)揮了巨大作用。例如，一些城市在公共交通、商場、醫(yī)院等公共場所部署了口罩佩戴檢測系統(tǒng)，通過實(shí)時(shí)監(jiān)控和提醒，有效提高了公眾佩戴口罩的意識和執(zhí)行率。這不僅有助于減少病毒的傳播風(fēng)險(xiǎn)，也提高了公眾的健康安全水平。DuringtheCOVID-19,facemaskwearingdetectiontechnologyplayedahugeroleinpublichealthmanagement.Forexample,somecitieshavedeployedmaskwearingdetectionsystemsinpublicplacessuchaspublictransportation,shoppingmalls,andhospitals.Throughreal-timemonitoringandreminders,thepublic'sawarenessandimplementationrateofwearingmaskshavebeeneffectivelyimproved.Thisnotonlyhelpstoreducetheriskofvirustransmission,butalsoimprovesthepublic'shealthandsafetylevel.在安全監(jiān)控領(lǐng)域，人臉口罩佩戴檢測技術(shù)同樣具有重要價(jià)值。例如，在一些銀行、珠寶店等高風(fēng)險(xiǎn)場所，安裝口罩佩戴檢測系統(tǒng)可以及時(shí)發(fā)現(xiàn)未佩戴口罩的入侵者，從而及時(shí)發(fā)出警報(bào)并啟動(dòng)相應(yīng)的安全措施。這項(xiàng)技術(shù)還可以用于監(jiān)控員工是否按照規(guī)定佩戴口罩，以確保工作場所的安全。Inthefieldofsecuritymonitoring,facialmaskwearingdetectiontechnologyalsohasimportantvalue.Forexample,inhigh-riskplacessuchasbanksandjewelrystores,installingamaskwearingdetectionsystemcanpromptlydetectintruderswhoarenotwearingmasks,therebyissuingalertsandinitiatingcorrespondingsecuritymeasures.Thistechnologycanalsobeusedtomonitorwhetheremployeesarewearingmasksaccordingtoregulationstoensureworkplacesafety.在商業(yè)應(yīng)用中，人臉口罩佩戴檢測技術(shù)也展現(xiàn)出了廣闊的應(yīng)用前景。例如，在零售店、超市等場所，通過部署口罩佩戴檢測系統(tǒng)，可以提醒顧客佩戴口罩，從而提高顧客的購物體驗(yàn)和健康保障。這項(xiàng)技術(shù)還可以用于分析顧客的購物行為和偏好，為商家提供精準(zhǔn)的市場營銷策略。Incommercialapplications,facialmaskwearingdetectiontechnologyhasalsoshownbroadapplicationprospects.Forexample,inretailstores,supermarketsandotherplaces,deployingamaskwearingdetectionsystemcanremindcustomerstowearmasks,therebyimprovingtheirshoppingexperienceandhealthprotection.Thistechnologycanalsobeusedtoanalyzecustomershoppingbehaviorandpreferences,providingmerchantswithprecisemarketingstrategies.除了以上幾個(gè)領(lǐng)域外，人臉口罩佩戴檢測技術(shù)還可以應(yīng)用于其他多個(gè)領(lǐng)域。例如，在醫(yī)療領(lǐng)域，這項(xiàng)技術(shù)可以用于監(jiān)測醫(yī)護(hù)人員的口罩佩戴情況，以確保醫(yī)療安全；在教育領(lǐng)域，可以用于監(jiān)督學(xué)生佩戴口罩的行為，保障校園安全；在交通領(lǐng)域，可以用于檢測司機(jī)是否佩戴口罩，以減少交通事故的風(fēng)險(xiǎn)。Inadditiontotheabove-mentionedfields,facialmaskwearingdetectiontechnologycanalsobeappliedtomultipleotherfields.Forexample,inthemedicalfield,thistechnologycanbeusedtomonitorthewearingofmasksbymedicalstafftoensuremedicalsafety;Inthefieldofeducation,itcanbeusedtosupervisethebehaviorofstudentswearingmasksandensurecampussafety;Inthefieldoftransportation,itcanbeusedtodetectwhetherdriversarewearingmaskstoreducetheriskoftrafficaccidents.人臉口罩佩戴檢測技術(shù)在各個(gè)領(lǐng)域都有著廣泛的應(yīng)用和巨大的潛力。隨著技術(shù)的不斷發(fā)展和完善，相信這項(xiàng)技術(shù)在未來將會發(fā)揮更加重要的作用。Facialmaskwearingdetectiontechnologyhasawiderangeofapplicationsandenormouspotentialinvariousfields.Withthecontinuousdevelopmentandimprovementoftechnology,webelievethatthistechnologywillplayamoreimportantroleinthefuture.五、未來發(fā)展趨勢展望OutlookonFutureDevelopmentTrends隨著和計(jì)算機(jī)視覺技術(shù)的不斷進(jìn)步，人臉口罩佩戴檢測在未來幾年內(nèi)將持續(xù)發(fā)展和改進(jìn)。隨著深度學(xué)習(xí)算法的優(yōu)化和新技術(shù)的出現(xiàn)，檢測系統(tǒng)的準(zhǔn)確性和效率有望得到顯著提高。Withthecontinuousadvancementofcomputervisiontechnology,facialmaskwearingdetectionwillcontinuetodevelopandimproveinthecomingyears.Withtheoptimizationofdeeplearningalgorithmsandtheemergenceofnewtechnologies,theaccuracyandefficiencyofdetectionsystemsareexpectedtobesignificantlyimproved.技術(shù)進(jìn)步：深度學(xué)習(xí)模型如卷積神經(jīng)網(wǎng)絡(luò)（CNN）和循環(huán)神經(jīng)網(wǎng)絡(luò)（RNN）的進(jìn)一步優(yōu)化，以及新型算法如生成對抗網(wǎng)絡(luò)（GAN）和自監(jiān)督學(xué)習(xí)方法的應(yīng)用，將為口罩佩戴檢測提供更強(qiáng)大的技術(shù)支持。邊緣計(jì)算技術(shù)的發(fā)展也將推動(dòng)實(shí)時(shí)、高效的人臉口罩佩戴檢測系統(tǒng)的實(shí)現(xiàn)。Technologicalprogress:FurtheroptimizationofdeeplearningmodelssuchasConvolutionalNeuralNetworks(CNN)andRecurrentNeuralNetworks(RNN),aswellastheapplicationofnewalgorithmssuchasGenerativeAdversarialNetworks(GAN)andSelfsupervisedLearningmethods,willprovidestrongertechnicalsupportformaskwearingdetection.Thedevelopmentofedgecomputingtechnologywillalsopromotetherealizationofreal-timeandefficientfacialmaskwearingdetectionsystem.數(shù)據(jù)質(zhì)量：高質(zhì)量的訓(xùn)練數(shù)據(jù)對于提高檢測模型的準(zhǔn)確性至關(guān)重要。隨著數(shù)據(jù)收集和標(biāo)注技術(shù)的進(jìn)步，以及公開數(shù)據(jù)集的不斷豐富，未來的口罩佩戴檢測系統(tǒng)將能夠處理更多樣化的場景和更復(fù)雜的情況。Dataquality:Highqualitytrainingdataiscrucialforimprovingtheaccuracyofdetectionmodels.Withtheadvancementofdatacollectionandannotationtechnology,aswellasthecontinuousenrichmentofpublicdatasets,futuremaskwearingdetectionsystemswillbeabletohandlemorediversescenariosandmorecomplexsituations.隱私保護(hù)：隨著人們對隱私保護(hù)的日益關(guān)注，未來的口罩佩戴檢測系統(tǒng)需要在確保準(zhǔn)確性和效率的同時(shí)，更加注重用戶隱私的保護(hù)。例如，通過采用差分隱私、聯(lián)邦學(xué)習(xí)等隱私保護(hù)技術(shù)，可以在不泄露用戶個(gè)人信息的前提下進(jìn)行模型訓(xùn)練和優(yōu)化。Privacyprotection:Withpeople'sincreasingattentiontoprivacyprotection,futuremaskwearingdetectionsystemsneedtopaymoreattentiontoprotectinguserprivacywhileensuringaccuracyandefficiency.Forexample,byadoptingprivacyprotectiontechnologiessuchasdifferentialprivacyandfederatedlearning,modeltrainingandoptimizationcanbecarriedoutwithoutdisclosinguserpersonalinformation.跨場景應(yīng)用：目前的口罩佩戴檢測系統(tǒng)主要關(guān)注于靜態(tài)圖像或視頻的檢測，未來的發(fā)展方向?qū)ǜ鼜V泛的跨場景應(yīng)用，如實(shí)時(shí)視頻流檢測、移動(dòng)設(shè)備上的實(shí)時(shí)檢測等。這將為公共場所的疫情防控、智能監(jiān)控等領(lǐng)域提供更多可能性。Crosssceneapplications:Currently,maskwearingdetectionsystemsmainlyfocusondetectingstaticimagesorvideos.Thefuturedevelopmentdirectionwillincludemoreextensivecrosssceneapplications,suchasreal-timevideostreamdetectionandreal-timedetectiononmobiledevices.Thiswillprovidemorepossibilitiesforepidemicpreventionandcontrol,intelligentmonitoring,andotherfieldsinpublicplaces.標(biāo)準(zhǔn)化與法規(guī)：隨著口罩佩戴檢測技術(shù)的廣泛應(yīng)用，相關(guān)的標(biāo)準(zhǔn)化和法規(guī)也將逐步完善。這將有助于規(guī)范技術(shù)的發(fā)展和應(yīng)用，保障其合法、合規(guī)地服務(wù)于社會。Standardizationandregulations:Withthewidespreadapplicationofmaskwearingdetectiontechnology,relevantstandardizationandregulationswillgraduallybeimproved.Thiswillhelpregulatethedevelopmentandapplicationoftechnology,ensuringitslegalandcompliantservicetosociety.人臉口罩佩戴檢測在未來將面臨諸多發(fā)展機(jī)遇和挑戰(zhàn)。隨著技術(shù)的不斷進(jìn)步和創(chuàng)新，我們有理由相信這一領(lǐng)域?qū)⑷〉酶语@著的成果，為疫情防控和智能監(jiān)控等領(lǐng)域的發(fā)展做出更大貢獻(xiàn)。Facialmaskwearingdetectionwillfacemanydevelopmentopportunitiesandchallengesinthefuture.Withthecontinuousprogressandinnovationoftechnology,wehavereasontobelievethatthisfieldwillachievemoresignificantresultsandmakegreatercontributionstothedevelopmentofepidemicpreventionandintelligentmonitoring.六、結(jié)論Conclusion隨著和計(jì)算機(jī)視覺技術(shù)的快速發(fā)展，人臉口罩佩戴檢測已成為當(dāng)前研究的熱點(diǎn)之一。本文綜述了近年來人臉口罩佩戴檢測的主要研究方法和進(jìn)展，并分析了其優(yōu)缺點(diǎn)。Withtherapiddevelopmentofcomputervisiontechnology,facialmaskwearingdetectionhasbecomeoneofthecurrentresearchhotspots.Thisarticlereviewsthemainresearchmethodsandprogressinfacemaskwearingdetectioninrecentyears,andanalyzestheiradvantagesanddisadvantages.從研究現(xiàn)狀來看，基于深度學(xué)習(xí)的方法在人臉口罩佩戴檢測中取得了顯著的成果。通過卷積神經(jīng)網(wǎng)絡(luò)等深度學(xué)習(xí)模型，可以有效提取人臉和口罩的特征，進(jìn)而實(shí)現(xiàn)準(zhǔn)確的口罩佩戴檢測。一些研究還引入了注意力機(jī)制、多模態(tài)信息融合等技術(shù)，進(jìn)一步提高了檢測的準(zhǔn)確性和魯棒性。Fromthecurrentresearchstatus,deeplearningbasedmethodshaveachievedsignificantresultsinfacialmaskwearingdetection.Throughdeeplearningmodelssuchasconvolutionalneuralnetworks,facialandmaskfeaturescanbeeffectivelyextracted,therebyachievingaccuratemaskwearingdetection.Somestudieshavealsointroducedtechniquessuchasattentionmechanismsandmultimodalinformationfusion,furtherimprovingtheaccuracyandrobustnessofdetection.然而，當(dāng)前的研究仍存在一些挑戰(zhàn)和問題需要解決。不同場景下的口罩佩戴檢測仍面臨一定的難度，特別是在低光照、遮擋等復(fù)雜環(huán)境下。現(xiàn)有的方法在處理口罩佩戴不規(guī)范、口罩類型多樣等問題時(shí)仍存在一定的局限性。隨著口罩佩戴檢測技術(shù)在實(shí)際應(yīng)用中的推廣，如何保護(hù)用戶隱私和數(shù)據(jù)安全也成為一個(gè)亟待解決的問題。However,therearestillsomechallengesandissuesthatneedtobeaddressedincurrentresear