基于低秩稀疏结构分解的纺织品瑕疵检测论文设计

摘要现有的纺织品瑕疵检测算法多采用传统的模式识别方法，如统计法、频谱法和训练法等。近些年来，低秩稀疏结构分解模型在显著性检测等领域得到了广泛的应用。低秩稀疏矩阵结构分解模型将待检测的特征图像矩阵分解为低秩矩阵和稀疏矩阵两个部分，其中低秩矩阵用来表示背景，稀疏矩阵用来表示稀疏矩阵。而纺织品在视觉上具有高度的冗余性，因此可以利用低秩稀疏结构分解来进行纺织品的瑕疵检测。本文以周期性纺织品为样本作为瑕疵检测的研究对象，采用低秩稀疏结构分解作为研究方法，所做的工作及研究成果如下：提出了一种基于模板校正与低秩分解的纺织品瑕疵检测方法。首先对原纺织品图像进行模板校正，以减轻图像拉伸、变形及光照对检测结果的影响；然后提出低秩校正分解模型，包含低秩项、稀疏项和校正项，采用交替方向法进行优化求解，将原特征矩阵分解为低秩矩阵和稀疏矩阵，其中低秩矩阵表示背景，稀疏矩阵表示瑕疵区域；最后利用最优阈值分割算法，对由稀疏矩阵产生的显著图进行阈值分割，得到二值化的检测结果。提出了一种基于权重低秩分解模型的纺织品瑕疵检测方法。采用块分割法获取瑕疵先验，瑕疵先验用于增加对大的瑕疵块的检测率。通过瑕疵先验来指导模型的分解，惩罚缺陷区域，降低算法的误检率，提高检测精度。提出了一种基于权重低秩分解与拉普拉斯正则项模型的纺织品瑕疵检测算法。在权重低秩分解模型的基础上加入拉普拉斯正则项，提高对细小瑕疵的检测精度，通过拉普拉斯正则项将具有相似像素的块共享相似的表示，不同的像素采用不同的表示方式，以此增加瑕疵与背景之间的距离。并且采用交替方向法对所提的凸优化模型进行优化求解，最后，采用自适应的阈值分割算法对由稀疏矩阵产生的显著图进行分割，定位出瑕疵区域。关键词:纺织品瑕疵检测;低秩分解;模板校正；拉普拉斯正则项1绪论1.1研究背景和意义在纺织工业中，纺织品的生产通常在针织机上进行ADDINZOTERO_ITEMCSL_CITATION{"citationID":"X2x21zmn","properties":{"formattedCitation":"\\super[1]\\nosupersub{}","plainCitation":"[1]","noteIndex":0},"citationItems":[{"id":215,"uris":["/users/local/xIMhsWAM/items/R7HS83TF"],"uri":["/users/local/xIMhsWAM/items/R7HS83TF"],"itemData":{"id":215,"type":"article-journal","container-title":"Optik","DOI":"10.1016/j.ijleo.2016.09.110","ISSN":"00304026","issue":"24","language":"en","page":"11960-11973","source":"Crossref","title":"Fabricdefectdetectionsystemsandmethods—Asystematicliteraturereview","volume":"127","author":[{"family":"Hanbay","given":"Kazım"},{"family":"Talu","given":"MuhammedFatih"},{"family":"Özgüven","given":"ÖmerFaruk"}],"issued":{"date-parts":[["2016",12]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[1]。因此，这个过程总会产生各种各样的瑕疵。研究表明，纺织品的瑕疵可以造成纺织品利润下降45%-65%ADDINZOTERO_ITEMCSL_CITATION{"citationID":"NsJCBLKu","properties":{"formattedCitation":"\\super[2]\\nosupersub{}","plainCitation":"[2]","noteIndex":0},"citationItems":[{"id":218,"uris":["/users/local/xIMhsWAM/items/YKWDZDBB"],"uri":["/users/local/xIMhsWAM/items/YKWDZDBB"],"itemData":{"id":218,"type":"article-journal","container-title":"Neurocomputing","DOI":"10.1016/j.neucom.2015.09.011","ISSN":"09252312","language":"en","page":"1386-1401","source":"Crossref","title":"Differentialevolution-basedoptimalGaborfiltermodelforfabricinspection","volume":"173","author":[{"family":"Tong","given":"Le"},{"family":"Wong","given":"W.K."},{"family":"Kwong","given":"C.K."}],"issued":{"date-parts":[["2016",1]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[2]。因此，在纺织品生产过程中，对其进行纺织品的瑕疵检测是不可缺少的一步。但由于纺织品图像本身具有复杂多变的纹理以及各式各样的瑕疵类型，给瑕疵检测算法的研究带来了一定的挑战性。在目前的工业生产中，纺织品的瑕疵检测主要采用人工检测的方式，如图1-1所示。但是人工检测存在许多的缺点，如，误检率高，检测速度慢，成本高等。然而，纺织品的自动瑕疵检测能弥补上述的这些缺点ADDINZOTERO_ITEMCSL_CITATION{"citationID":"FKAtLEgL","properties":{"formattedCitation":"\\super[3]\\nosupersub{}","plainCitation":"[3]","noteIndex":0},"citationItems":[{"id":217,"uris":["/users/local/xIMhsWAM/items/32JYVAL9"],"uri":["/users/local/xIMhsWAM/items/32JYVAL9"],"itemData":{"id":217,"type":"article-journal","container-title":"IEEETransactionsonPatternAnalysisandMachineIntelligence","DOI":"10.1109/TPAMI.1982.4767309","ISSN":"0162-8828","issue":"6","page":"557-573","source":"Crossref","title":"AutomatedVisualInspection:ASurvey","title-short":"AutomatedVisualInspection","volume":"PAMI-4","author":[{"family":"Chin","given":"RolandT."},{"family":"Harlow","given":"CharlesA."}],"issued":{"date-parts":[["1982",11]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[3]。现如今，越来越多的纺织品企业采用了自动化瑕疵检测设备，检测速度可以达到每分钟120米，并且检测成功率一般在90%左右ADDINZOTERO_ITEMCSL_CITATION{"citationID":"M6QjD9Dp","properties":{"formattedCitation":"\\super[4]\\nosupersub{}","plainCitation":"[4]","noteIndex":0},"citationItems":[{"id":130,"uris":["/users/local/xIMhsWAM/items/VAQKQEI6"],"uri":["/users/local/xIMhsWAM/items/VAQKQEI6"],"itemData":{"id":130,"type":"article-journal","container-title":"IEEETransactionsonIndustryApplications","issue":"5","page":"1267-1276","title":"FabricdefectdetectionbyFourieranalysis","volume":"36","author":[{"family":"Chan","given":"ChiHo"},{"family":"Pang","given":"G.K.H."}],"issued":{"date-parts":[["2000"]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[4]。对于各种各样的瑕疵类型，自动瑕疵检测系统都有统一的评价标准，避免了主观判断给检测带来的影响。近年来，随着低秩稀疏结构分解模型的快速发展，利用低秩稀疏结构分解模型进行显著性检测日渐成熟。低秩稀疏结构分解模型的原理是将图像的特征数据矩阵分解为低秩矩阵和稀疏矩阵，用低秩矩阵来表示背景，用稀疏矩阵来表示显著性区域。如利用低秩稀疏结构分解模型进行目标检测ADDINZOTERO_ITEMCSL_CITATION{"citationID":"a1vnsnot92p","properties":{"formattedCitation":"\\super[5,6]\\nosupersub{}","plainCitation":"[5,6]","noteIndex":0},"citationItems":[{"id":312,"uris":["/users/local/xIMhsWAM/items/P39KSXI5"],"uri":["/users/local/xIMhsWAM/items/P39KSXI5"],"itemData":{"id":312,"type":"article-journal","abstract":"Low-rankrecoverymodelshaveshownpotentialforsalientobjectdetection,whereamatrixisdecomposedintoalow-rankmatrixrepresentingimagebackgroundandasparsematrixidentifyingsalientobjects.Twodeficiencies,however,stillexist.First,previousworktypicallyassumestheelementsinthesparsematrixaremutuallyindependent,ignoringthespatialandpatternrelationsofimageregions.Second,whenthelow-rankandsparsematricesarerelativelycoherent,e.g.,whentherearesimilaritiesbetweenthesalientobjectsandbackgroundorwhenthebackgroundiscomplicated,itisdifficultforpreviousmodelstodisentanglethem.Toaddresstheseproblems,weproposeanovelstructuredmatrixdecompositionmodelwithtwostructuralregularizations:(1)atree-structuredsparsity-inducingregularizationthatcapturestheimagestructureandenforcespatchesfromthesameobjecttohavesimilarsaliencyvalues,and(2)aLaplacianregularizationthatenlargesthegapsbetweensalientobjectsandthebackgroundinfeaturespace.Furthermore,high-levelpriorsareintegratedtoguidethematrixdecompositionandboostthedetection.Weevaluateourmodelforsalientobjectdetectiononfivechallengingdatasetsincludingsingleobject,multipleobjectsandcomplexsceneimages,andshowcompetitiveresultsascomparedwith24state-of-the-artmethodsintermsofsevenperformancemetrics.","container-title":"IEEETransactionsonPatternAnalysis&MachineIntelligence","DOI":"10.1109/TPAMI.2016.2562626","issue":"4","page":"818-832","source":"BaiduScholar","title":"SalientObjectDetectionviaStructuredMatrixDecomposition","volume":"39","author":[{"family":"Peng","given":"Houwen"},{"family":"Li","given":"Bing"},{"family":"Ling","given":"Haibin"},{"family":"Hu","given":"Weiming"},{"family":"Xiong","given":"Weihua"},{"family":"Maybank","given":"StephenJ."}],"issued":{"date-parts":[["2017"]]}},"label":"page"},{"id":434,"uris":["/users/local/xIMhsWAM/items/9SJHRW7G"],"uri":["/users/local/xIMhsWAM/items/9SJHRW7G"],"itemData":{"id":434,"type":"article-journal","container-title":"IEEESignalProcessingLetters","issue":"8","note":"publisher:IEEE","page":"739–742","title":"Visualsaliencydetectionviasparsitypursuit","volume":"17","author":[{"family":"Yan","given":"Junchi"},{"family":"Zhu","given":"Mengyuan"},{"family":"Liu","given":"Huanxi"},{"family":"Liu","given":"Yuncai"}],"issued":{"date-parts":[["2010"]]}},"label":"page"}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[5,6]、人脸检测ADDINZOTERO_ITEMCSL_CITATION{"citationID":"aldsb4j4uo","properties":{"formattedCitation":"\\super[7]\\nosupersub{}","plainCitation":"[7]","noteIndex":0},"citationItems":[{"id":435,"uris":["/users/local/xIMhsWAM/items/NCMGC9DJ"],"uri":["/users/local/xIMhsWAM/items/NCMGC9DJ"],"itemData":{"id":435,"type":"article-journal","container-title":"Patternrecognition","issue":"11","note":"publisher:Elsevier","page":"3502–3511","title":"Fisherdiscriminationbasedlowrankmatrixrecoveryforfacerecognition","volume":"47","author":[{"family":"Zheng","given":"Zhonglong"},{"family":"Yu","given":"Mudan"},{"family":"Jia","given":"Jiong"},{"family":"Liu","given":"Huawen"},{"family":"Xiang","given":"Daohong"},{"family":"Huang","given":"Xiaoqiao"},{"family":"Yang","given":"Jie"}],"issued":{"date-parts":[["2014"]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[7]等。纺织品图像是一种人造的纹理，虽然形态各异，结构复杂，但是其中存在着大量的视觉冗余，其图案由重复的纹理单元构成，而纺织品的瑕疵是相对孤立的，是稀疏的。因此纺织品的瑕疵检测相对于目前的显著性检测，更好地符合了低秩稀疏结构分解模型的特性。但是基于低秩稀疏结构分解的显著性检测算法大都是针对自然场景中的图像，其特征提取方式重点考虑了色差、亮度等特性。而纺织品图像由于存在着拉伸变形、光照等问题，不能直接应用以往的显著性检测算法。因此，需要提出一种适用于纺织品瑕疵检测领域的低秩稀疏结构分解模型，实现背景与瑕疵区域的分离。目前根据纺织品种类的不同，可以将纺织品分为两大类，第一类是背景简单没有复杂图案的纺织品，如平纹和斜纹，第二类是背景复杂且具有周期变化图案的纺织品，根据周期图案的不同又可细分为盒状，星状和点状纺织品。几种常见的纺织品瑕疵类型如图1-2所示，其中第一、二幅图像为第一类纺织品图像，后三幅为第二类纺织品图像。第一类纺织品图像的检测相对简单，目前已经有许多成熟有效的算法。而第二类周期性纺织品图像的瑕疵检测则更为复杂，通常是具有花纹的纺织品，如，地毯、床罩等。Ng等人ADDINZOTERO_ITEMCSL_CITATION{"citationID":"qS0fBlu2","properties":{"formattedCitation":"\\super[8]\\nosupersub{}","plainCitation":"[8]","noteIndex":0},"citationItems":[{"id":346,"uris":["/users/local/xIMhsWAM/items/NK4DXLPZ"],"uri":["/users/local/xIMhsWAM/items/NK4DXLPZ"],"itemData":{"id":346,"type":"article-journal","abstract":"©2017SocietyforIndustrialandAppliedMathematics.Inthispaper,westudyanimagedecompositionmodelforpatternedfabricinspection.Itisimportanttorepresentfabricpatternseffectivelysothatfabricdefectscanbeseparated.Oneconcernisthatbothpatternedfabric(e.g.,star-orbox-patternedfabrics)andfabricdefectscontainmainlylowfrequencycomponents.ThemainideaofthispaperistousetheconvolutionofalatticewithaDiraccombtocharacterizeapatternedfabricimagesothatitsrepetitivecomponentscanbeeffectivelyrepresentedintheimagedecompositionmodel.Weformulateamodelwithtotalvariation,sparsity,andlow-ranktermsforpatternedfabricinspection.Thetotalvariationtermisusedtoregularizethedefectiveimage,andthesparsityandthelow-ranktermsareemployedtocontroltheDiraccombfunction.Theproposedmodelcanbesolvedefficientlyviaaconvexprogrammingsolver.Ourexperimentalresultsfordifferenttypesofpatternedfabricsshowthattheproposedmodelcaninspectdefectsatahigheraccuracycomparedwithsomeclassicalmethodsintheliterature.","container-title":"SIAMJournalonImagingSciences","DOI":"10.1137/17M1113138","issue":"4","page":"2140-2164","source":"BaiduScholar","title":"Lattice-BasedPatternedFabricInspectionbyUsingTotalVariationwithSparsityandLow-RankRepresentations","volume":"10","author":[{"family":"Ng","given":"MichaelK."},{"family":"Ngan","given":"HenryY.T."},{"family":"Yuan","given"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