【《基于机器视觉的条状表面缺陷检测研究国内外文献综述》4200字】

基于机器视觉的条状表面缺陷检测研究国内外文献综述机器视觉是对计算机视觉技术的工程化ADDINEN.CITE<EndNote><Cite><Author>张广军</Author><Year>2005</Year><RecNum>175</RecNum><DisplayText><styleface="superscript">[4]</style></DisplayText><record><rec-number>175</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646313337">175</key></foreign-keys><ref-typename="BookSection">5</ref-type><contributors><authors><author><styleface="normal"font="default"charset="134"size="100%">张广军</style></author></authors></contributors><titles><title><styleface="normal"font="default"charset="134"size="100%">机器视觉</style></title></titles><pages>1</pages><volume>6</volume><dates><year>2005</year></dates><pub-location><styleface="normal"font="default"charset="134"size="100%">北京</style></pub-location><publisher><styleface="normal"font="default"charset="134"size="100%">科学出版社</style></publisher><urls></urls></record></Cite></EndNote>[4]，广泛应用于工业缺陷检测领域。完整的视觉缺陷检测系统通常包含如下模块：（1）图像获取模块；（2）图像处理模块；（3）图像分析模块；（4）数据管理模块；（5）人机界面ADDINEN.CITE<EndNote><Cite><Author>TangB</Author><Year>2017</Year><RecNum>116</RecNum><DisplayText><styleface="superscript">[5]</style></DisplayText><record><rec-number>116</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1618994922">116</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>TangB,KongJY,WuSQ</author></authors></contributors><titles><title>Reviewofsurfacedefectdetectionbasedonmachinevision</title><secondary-title>JournalofImageandGraphics</secondary-title></titles><periodical><full-title>JournalofImageandGraphics</full-title></periodical><pages>1640-1663</pages><volume>22</volume><num-vols>12</num-vols><dates><year>2017</year></dates><urls></urls></record></Cite></EndNote>[5]。本节首先对国内外视觉检测系统及相关缺陷检测算法的研究现状进行综述，接着对条状表面缺陷检测算法的有关研究进行分析。1.1工业表面缺陷检测研究现状TC"1.1StatusofResearchonIndustrialSurfaceDefectDetection"\l3国外对视觉缺陷检测系统的研究起步较早。1983年，美国Honeywell公司设计了一款基于线阵CCD（Charge-coupledDevice）的缺陷检测系统，它引进了专用图像阵列处理机用于分析缺陷，然后运用基于树分类器和句法模式识别的分类器来判定连铸板坯表面缺陷ADDINEN.CITE<EndNote><Cite><Author>Suresh</Author><Year>1983</Year><RecNum>177</RecNum><DisplayText><styleface="superscript">[6]</style></DisplayText><record><rec-number>177</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646360559">177</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Suresh,BindinganavleR</author><author>Fundakowski,RichardA</author><author>Levitt,TodS</author><author>Overland,JohnE</author></authors></contributors><titles><title>Areal-timeautomatedvisualinspectionsystemforhotsteelslabs</title><secondary-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</secondary-title></titles><periodical><full-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</full-title></periodical><pages>563-572</pages><number>6</number><dates><year>1983</year></dates><isbn>0162-8828</isbn><urls></urls></record></Cite></EndNote>[6]。之后，Westinghouse公司开拓了将明域、暗域、微光域三种照明光路形式组合应用于带钢表面缺陷检测的新思路ADDINEN.CITE<EndNote><Cite><Author>Porter</Author><Year>1988</Year><RecNum>180</RecNum><DisplayText><styleface="superscript">[7]</style></DisplayText><record><rec-number>180</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646366026">180</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Porter,T.F.</author><author>Sylvester,R.A.</author><author>Bouyoucas,T.W.</author><author>Kolesar,M.P.</author></authors></contributors><titles><title>Automaticstripsurfacedefectdetectionsystem</title></titles><dates><year>1988</year></dates><urls></urls></record></Cite></EndNote>[7]。英国EuropeanElectronicSystem公司针对连轧环境下的钢板质量检定，研发了实时性高、可靠性强的EES系统ADDINEN.CITE<EndNote><Cite><Author>Park</Author><Year>1995</Year><RecNum>182</RecNum><DisplayText><styleface="superscript">[8]</style></DisplayText><record><rec-number>182</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646376364">182</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Park,DavidG</author><author>Levoi,MartinP</author><author>VanHaneghem,AI</author></authors></contributors><titles><title>Practicalapplicationofon-linehotstripinspectionsystematHoogovens</title><secondary-title>IronSteelEng.(USA)</secondary-title></titles><periodical><full-title>IronSteelEng.(USA)</full-title></periodical><pages>40-43</pages><volume>72</volume><number>7</number><dates><year>1995</year></dates><isbn>0021-1559</isbn><urls></urls></record></Cite></EndNote>[8]，先后被多个公司，如荷兰Hoogovens钢铁公司、美国Inland钢铁公司应用。同一时期，机器视觉行业巨头Cognex公司先后提出SmartView系统、线扫描系统技术、iS-2000自动检测系统ADDINEN.CITE<EndNote><Cite><Author>Rodrick</Author><Year>1998</Year><RecNum>181</RecNum><DisplayText><styleface="superscript">[9]</style></DisplayText><record><rec-number>181</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646376216">181</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Rodrick,TJ</author></authors></contributors><titles><title>Softwarecontrolledon-linesurfaceinspection</title><secondary-title>SteelTimesInt</secondary-title></titles><periodical><full-title>SteelTimesInt</full-title></periodical><pages>30</pages><volume>22</volume><number>3</number><dates><year>1998</year></dates><urls></urls></record></Cite></EndNote>[9]和iLearn自学习分类器系统ADDINEN.CITE<EndNote><Cite><Author>Carisetti</Author><Year>1998</Year><RecNum>179</RecNum><DisplayText><styleface="superscript">[10]</style></DisplayText><record><rec-number>179</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646362516">179</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Carisetti,C</author></authors></contributors><titles><title>iLearnself-learningdefectclassifier</title><secondary-title>IronandSteelEngineer(USA)</secondary-title></titles><periodical><full-title>IronandSteelEngineer(USA)</full-title></periodical><pages>50-53</pages><volume>75</volume><number>8</number><dates><year>1998</year></dates><isbn>0021-1559</isbn><urls></urls></record></Cite></EndNote>[10]，分别用于零组件的识别、镀锌生产线、钢板缺陷检测，优化了自学习分类方法在训练数据、特征选择等方面的性能，并显著提高了计算速度和数据吞吐量。国内对视觉检测系统的研究开始较晚。20世纪80年代末，华中理工大学罗志勇团队提出了面阵CCD融合DSP图像处理平台的缺陷检测系统，以应对冷轧带钢自动质量检验的难题，此外该系统还能够完成最小带宽的量度ADDINEN.CITE<EndNote><Cite><Author>罗志勇</Author><Year>1996</Year><RecNum>183</RecNum><DisplayText><styleface="superscript">[11]</style></DisplayText><record><rec-number>183</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646376778">183</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>罗志勇</author><author>刘栋玉</author></authors></contributors><titles><title>新型冷轧带钢表面缺陷在线检测系统</title><secondary-title>华中理工大学学报</secondary-title></titles><periodical><full-title>华中理工大学学报</full-title></periodical><pages>4</pages><volume>24</volume><number>1</number><dates><year>1996</year></dates><urls></urls></record></Cite></EndNote>[11]。之后，北京科技大学徐科课题组也开展了钢板表面质量检测的相关工作，提出的缺陷检测系统对“锈痕”、“边裂”等6种钢板表面缺陷有较好的辨别力ADDINEN.CITE<EndNote><Cite><Author>徐科</Author><Year>2000</Year><RecNum>187</RecNum><DisplayText><styleface="superscript">[12]</style></DisplayText><record><rec-number>187</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646377500">187</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>徐科</author><author>徐金梧</author><author>鹿守理</author><author>郭芳</author></authors></contributors><titles><title>冷轧带钢表面自动监测系统的研究</title><secondary-title>钢铁</secondary-title></titles><periodical><full-title>钢铁</full-title></periodical><pages>4</pages><volume>35</volume><number>10</number><dates><year>2000</year></dates><urls></urls></record></Cite></EndNote>[12]。此外，华中科技大学ADDINEN.CITE<EndNote><Cite><Author>彭向前</Author><Year>2009</Year><RecNum>188</RecNum><DisplayText><styleface="superscript">[13]</style></DisplayText><record><rec-number>188</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646378051">188</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>彭向前</author><author>谢经明</author><author>陆万顺</author><author>陈幼平</author></authors></contributors><titles><title>浮法玻璃质量在线检测与分析系统</title><secondary-title>玻璃</secondary-title></titles><periodical><full-title>玻璃</full-title></periodical><pages>4</pages><volume>36</volume><number>12</number><dates><year>2009</year></dates><urls></urls></record></Cite></EndNote>[13]、上海宝钢集团ADDINEN.CITE<EndNote><Cite><Author>何永辉</Author><Year>2007</Year><RecNum>184</RecNum><DisplayText><styleface="superscript">[14]</style></DisplayText><record><rec-number>184</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646377078">184</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>何永辉</author><author>王康健</author><author>石桂芬</author></authors></contributors><titles><title>基于机器视觉的高速带钢孔洞检测系统</title><secondary-title>应用光学</secondary-title></titles><periodical><full-title>应用光学</full-title></periodical><pages>5</pages><volume>28</volume><number>3</number><dates><year>2007</year></dates><urls></urls></record></Cite></EndNote>[14]等科研院所也相继对表面缺陷在线检测系统进行探索研究，做了大量的实验工作，并取得了一定的成果。随着视觉系统研究的不断深入，其核心模块——图像处理及分析模块的相关知识体系也愈发完善，对应本文研究的工业表面缺陷检测算法。目前，工业表面缺陷检测方法主要有两类，分别是基于传统视觉的检测方法和基于深度学习的检测方法。基于传统视觉的检测方法需要人为设计特征提取的方式，即显式提取特征，该类方法大致需要经历四个步骤：（1）图像预处理；（2）图像分割；（3）特征提取及选取；（4）缺陷识别ADDINEN.CITE<EndNote><Cite><Author>TangB</Author><Year>2017</Year><RecNum>116</RecNum><DisplayText><styleface="superscript">[5]</style></DisplayText><record><rec-number>116</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1618994922">116</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>TangB,KongJY,WuSQ</author></authors></contributors><titles><title>Reviewofsurfacedefectdetectionbasedonmachinevision</title><secondary-title>JournalofImageandGraphics</secondary-title></titles><periodical><full-title>JournalofImageandGraphics</full-title></periodical><pages>1640-1663</pages><volume>22</volume><num-vols>12</num-vols><dates><year>2017</year></dates><urls></urls></record></Cite></EndNote>[5]。图像预处理常采用直方图均衡、灰度线性变换、基于空域或频域的滤波器等方法来减少图像噪声、提高图像质量。图像分割通常借助阈值划分、边缘检测ADDINEN.CITE<EndNote><Cite><Author>Canny</Author><Year>1986</Year><RecNum>189</RecNum><DisplayText><styleface="superscript">[15]</style></DisplayText><record><rec-number>189</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646378669">189</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Canny,John</author></authors></contributors><titles><title>AComputationalApproachtoEdgeDetection</title><secondary-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</secondary-title></titles><periodical><full-title>IEEETransactionsonPatternAnalysisandMachineIntelligence</full-title></periodical><pages>679-698</pages><volume>PAMI-8</volume><number>6</number><dates><year>1986</year></dates><urls></urls></record></Cite></EndNote>[15]、小波变换ADDINEN.CITE<EndNote><Cite><Author>Daubechies</Author><Year>1990</Year><RecNum>190</RecNum><DisplayText><styleface="superscript">[16]</style></DisplayText><record><rec-number>190</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646378831">190</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Daubechies,I.</author></authors></contributors><titles><title>Thewavelettransform,time-frequencylocalizationandsignalanalysis</title><secondary-title>IEEETransactionsonInformationTheory</secondary-title></titles><periodical><full-title>IEEETransactionsonInformationTheory</full-title></periodical><pages>961-1005</pages><volume>36</volume><number>5</number><dates><year>1990</year></dates><urls></urls></record></Cite></EndNote>[16]等方法将图像分离为多个互不相交的区域，以得到感兴趣区域（ROI）。特征提取从纹理、颜色、形状等方面构建特征描述，主要手段有结构法、统计法、滤波法和模型法。主成分分析（PCA）、Fisher分析法（FDA）等方法常用于特征选择。在特征提取及选择的基础上，可使用阈值判断法、贝叶斯决策、SVM分类器、聚类、BP网络等方法识别缺陷。基于上述理论和方法，工业各领域的表面缺陷检测算法相继涌现。在竹条缺陷检测方面，贺峰等人首先运用边缘检测算法对三角条缺陷进行检测，然后利用小波分解和图像共生矩阵滤除竹条纹理，从而减少背景纹理的干扰，最后使用OSTU自动阈值检测方法识别霉烂、裂缝等5种缺陷ADDINEN.CITE<EndNote><Cite><Author>贺峰;秦现生;刘琼;宋昕</Author><Year>2010</Year><RecNum>71</RecNum><DisplayText><styleface="superscript">[17]</style></DisplayText><record><rec-number>71</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1608363898">71</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author><styleface="normal"font="default"charset="134"size="100%">贺峰</style><styleface="normal"font="default"size="100%">;</style><styleface="normal"font="default"charset="134"size="100%">秦现生</style><styleface="normal"font="default"size="100%">;</style><styleface="normal"font="default"charset="134"size="100%">刘琼</style><styleface="normal"font="default"size="100%">;</style><styleface="normal"font="default"charset="134"size="100%">宋昕</style></author></authors></contributors><titles><title><styleface="normal"font="default"size="100%"></style><styleface="normal"font="default"charset="134"size="100%">基于小波多尺度分解的竹条缺陷检测算法</style></title><secondary-title><styleface="normal"font="default"size="100%"></style><styleface="normal"font="default"charset="134"size="100%">机电一体化</style></secondary-title></titles><pages>46-49</pages><dates><year>2010</year></dates><urls></urls></record></Cite></EndNote>[17]。黄炳强首先采用霍夫变换和线性变换对图像进行角度校正，然后针对不同类型的竹条缺陷设计不同的检测方法，如使用形态学方法检测虫洞缺陷，用自适应Canny双阈值方法检测裂纹，另外还从灰度、纹理等方面构建竹条青黄面的特征向量，并结合BP神经网络对竹条青黄面进行检测ADDINEN.CITE<EndNote><Cite><Author>黄炳强</Author><Year>2018</Year><RecNum>76</RecNum><DisplayText><styleface="superscript">[18]</style></DisplayText><record><rec-number>76</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1608374855">76</key></foreign-keys><ref-typename="Thesis">32</ref-type><contributors><authors><author><styleface="normal"font="default"charset="134"size="100%">黄炳强</style></author></authors></contributors><titles><title><styleface="normal"font="default"charset="134"size="100%">基于机器视觉的长竹条表面缺陷检测及颜色分类研究</style></title></titles><volume><styleface="normal"font="default"charset="134"size="100%">硕士</style></volume><dates><year>2018</year></dates><publisher><styleface="normal"font="default"charset="134"size="100%">广西师范大学</style></publisher><urls></urls></record></Cite></EndNote>[18]。Kuang等人首先使用阈值化方法提取感兴趣区域，然后利用LBP和GLCM技术提取特征，最后用SVM分类器识别竹条缺陷ADDINEN.CITEADDINEN.CITE.DATA[19]。在其它领域，Stojanovic等人通过阈值化方法和灰度差法（Grayleveldifferencemethod，GLDM）提取纺织物表面缺陷的几何特征和统计特征，再结合前馈神经网络进行分类，取得了较好的效果ADDINEN.CITE<EndNote><Cite><Author>Stojanovic</Author><Year>2001</Year><RecNum>192</RecNum><DisplayText><styleface="superscript">[20]</style></DisplayText><record><rec-number>192</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646379919">192</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Stojanovic,Radovan</author><author>Mitropulos,Panagiotis</author><author>Koulamas,Christos</author><author>Karayiannis,Yorgos</author><author>Koubias,Stavros</author><author>Papadopoulos,George</author></authors></contributors><titles><title>Real-timevision-basedsystemfortextilefabricinspection</title><secondary-title>Real-TimeImaging</secondary-title></titles><periodical><full-title>Real-TimeImaging</full-title></periodical><pages>507-518</pages><volume>7</volume><number>6</number><dates><year>2001</year></dates><isbn>1077-2014</isbn><urls></urls></record></Cite></EndNote>[20]。赵君爱提出基于加权中值滤波和均值滤波的噪声滤除算法、基于改进数据场的模糊C均值聚类(FCM)目标分割算法和基于模糊k近邻的LBP特征提取算法以优化图像预处理、图像分割、特征提取步骤，这些方法应用于冷轧钢板和焊缝表面缺陷检测取得了显著的改进ADDINEN.CITE<EndNote><Cite><Author>赵君爱</Author><Year>2016</Year><RecNum>194</RecNum><DisplayText><styleface="superscript">[21]</style></DisplayText><record><rec-number>194</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646380310">194</key></foreign-keys><ref-typename="Thesis">32</ref-type><contributors><authors><author>赵君爱</author></authors></contributors><titles><title>基于图像处理的工件表面缺陷检测理论与方法研究</title></titles><dates><year>2016</year></dates><publisher>东南大学</publisher><urls></urls></record></Cite></EndNote>[21]。Sa等人对OSTU分割算法进行优化，提出一种基于位置的自适应阈值分割算法，并设计了Run-based连通区域标注方法，从而提升了检测的速度，用于石英棒缺陷检测取得了较高的准确率ADDINEN.CITE<EndNote><Cite><Author>Sa</Author><Year>2018</Year><RecNum>195</RecNum><DisplayText><styleface="superscript">[22]</style></DisplayText><record><rec-number>195</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646380869">195</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Sa,J.</author><author>An,Y.</author><author>Ye,F.</author><author>Shi,L.</author><author>Wan,S.</author></authors></contributors><titles><title>CorrugatedPipeDefectDetectionBasedonDigitalImageProcessing</title><secondary-title>20185thInternationalConferenceonSystemsandInformatics(ICSAI)</secondary-title></titles><dates><year>2018</year></dates><urls></urls></record></Cite></EndNote>[22]。吕明珠提出基于K-means聚类的十字线分割和基于区域生长的四色圆提取方法，并使用尺度不变特征变换（SIFT）结合决策树的方法对缺陷进行分类，在印刷品表面缺陷检测应用中有着突出的表现ADDINEN.CITE<EndNote><Cite><Author>吕明珠</Author><Year>2019</Year><RecNum>196</RecNum><DisplayText><styleface="superscript">[23]</style></DisplayText><record><rec-number>196</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646381154">196</key></foreign-keys><ref-typename="Thesis">32</ref-type><contributors><authors><author><styleface="normal"font="default"charset="134"size="100%">吕明珠</style></author></authors></contributors><titles><title><styleface="normal"font="default"charset="134"size="100%">基于机器视觉的印刷品表面缺陷检测研究</style></title></titles><dates><year>2019</year></dates><publisher><styleface="normal"font="default"charset="134"size="100%">西安理工大学</style></publisher><urls></urls></record></Cite></EndNote>[23]。基于传统视觉的检测方法的特征提取步骤大都需要精心设计，针对性较强，对某一类或某几类具有良好的检测效果，但不能很好地迁移到其它场景，存在适应性差、泛化能力不足、成像条件苛刻、开发周期长等问题。此外，一些企业研发了集成传统图像处理算法的视觉软件以实现缺陷检测，如HALCON、HexSight、VisionPro，然而这些商业化软件往往价格过高，导致一些中小型企业承担不起费用。基于深度学习的检测方法主流是以卷积神经网络为基础提出的各类检测器。自2012年卷积神经网络AlexNetADDINEN.CITE<EndNote><Cite><Author>Krizhevsky</Author><Year>2012</Year><RecNum>197</RecNum><DisplayText><styleface="superscript">[24]</style></DisplayText><record><rec-number>197</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646381343">197</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Krizhevsky,Alex</author><author>Sutskever,Ilya</author><author>Hinton,GeoffreyE</author></authors></contributors><titles><title>Imagenetclassificationwithdeepconvolutionalneuralnetworks</title><secondary-title>Advancesinneuralinformationprocessingsystems</secondary-title></titles><periodical><full-title>AdvancesinNeuralInformationProcessingSystems</full-title></periodical><volume>25</volume><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>[24]在计算机视觉大赛ILSVRC斩获冠军，各界掀起了对卷积神经网络的研究浪潮，该类网络“局部连接”、“参数共享”的特点使之能够高效地完成对图像矩阵的计算。基于卷积神经网络的目标检测器以自动提取特征的优势替代传统机器视觉的显式提取特征，为工业表面缺陷检测注入了新鲜血液，在铝材ADDINEN.CITE<EndNote><Cite><Author>张磊</Author><RecNum>198</RecNum><DisplayText><styleface="superscript">[25]</style></DisplayText><record><rec-number>198</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646381940">198</key></foreign-keys><ref-typename="Thesis">32</ref-type><contributors><authors><author>张磊</author></authors></contributors><titles><title>深度学习在铝型材表面缺陷检测中的应用研究</title></titles><dates></dates><publisher>合肥工业大学</publisher><urls></urls></record></Cite></EndNote>[25]、木材ADDINEN.CITE<EndNote><Cite><Author>Liu</Author><Year>2019</Year><RecNum>199</RecNum><DisplayText><styleface="superscript">[26]</style></DisplayText><record><rec-number>199</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646383322">199</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Liu,Ying</author><author>Zhou,XiaoLin</author><author>Hu,ZhongKang</author><author>Yu,YaBin</author><author>Yang,YuTu</author><author>Xu,ChengYi</author></authors></contributors><titles><title>Wooddefectrecognitionbasedonoptimizedconvolutionneuralnetworkalgorithm</title><secondary-title>JournalofForestryEngineering</secondary-title></titles><periodical><full-title>JournalofForestryEngineering</full-title></periodical><pages>115-120</pages><volume>4</volume><number>1</number><dates><year>2019</year></dates><isbn>2096-1359</isbn><urls></urls></record></Cite></EndNote>[26]、印刷电路板ADDINEN.CITEADDINEN.CITE.DATA[27,28]等多个工业领域取得重大突破。在竹条缺陷检测方面，高钦泉等人通过引入额外的辅助检测网络对一阶段检测器CenterNet进行优化，并且设计注意力连接方式实现新模块与主干网络的融合，优化后的网络可以有效提升小数据规模应用中的检测性能ADDINEN.CITEADDINEN.CITE.DATA[29]。Hao等人设计了基于FasterRCNN的钢铁缺陷检测算法，该算法引进了平衡特征金字塔（BalancedFeaturePyramid，BFP）和可变形卷积网络（DeformableConvolutionNetwork，DCN），使得网络能够更好地进行特征提取及融合，该算法在公开数据集NEU-DET较原生FasterRCNN提升了14.4%ADDINEN.CITE<EndNote><Cite><Author>Hao</Author><Year>2020</Year><RecNum>92</RecNum><DisplayText><styleface="superscript">[30]</style></DisplayText><record><rec-number>92</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1609144136">92</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Hao,Ruiyang</author><author>Lu,Bingyu</author><author>Cheng,Ying</author><author>Li,Xiu</author><author>Huang,Biqing</author></authors></contributors><titles><title>ASteelSurfaceDefectInspectionApproachtowardsSmartIndustrialMonitoring</title><secondary-title>JournalofIntelligentManufacturing</secondary-title></titles><periodical><full-title>JournalofIntelligentManufacturing</full-title></periodical><number>9</number><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>[30]。Zhang等人对YOLOv3网络进行改良，并将其应用于桥梁表面缺陷检测，首先借鉴了迁移学习的思想，从相似数据集训练得到预训练权重，然后使用归一化技术和Focalloss损失函数以提升网络的收敛速度和检测精度ADDINEN.CITE<EndNote><Cite><Author>Zhang</Author><Year>2020</Year><RecNum>200</RecNum><DisplayText><styleface="superscript">[31]</style></DisplayText><record><rec-number>200</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646383592">200</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Zhang,Chaobo</author><author>Chang,Chih‐chen</author><author>Jamshidi,Maziar</author></authors></contributors><titles><title>Concretebridgesurfacedamagedetectionusingasingle‐stagedetector</title><secondary-title>Computer‐AidedCivilandInfrastructureEngineering</secondary-title></titles><periodical><full-title>Computer‐AidedCivilandInfrastructureEngineering</full-title></periodical><pages>389-409</pages><volume>35</volume><number>4</number><dates><year>2020</year></dates><isbn>1093-9687</isbn><urls></urls></record></Cite></EndNote>[31]。类似地，吕永发也对YOLOv3网络进行了优化，并将其用于手机表面缺陷检测，作者首先对检测器的主干网络进行浅层的轻量化设计，然后运用GIOU边框损失函数指导网络训练，优化后的网络在检测准确率和运行速率之间达到了较好的平衡，具有较高的可行性ADDINEN.CITE<EndNote><Cite><Author>吕永发</Author><RecNum>201</RecNum><DisplayText><styleface="superscript">[32]</style></DisplayText><record><rec-number>201</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646383688">201</key></foreign-keys><ref-typename="Thesis">32</ref-type><contributors><authors><author>吕永发</author></authors></contributors><titles><title>基于深度学习的手机表面缺陷检测算法</title></titles><dates></dates><publisher>郑州大学</publisher><urls></urls></record></Cite></EndNote>[32]。基于卷积神经网络的目标检测器具有自主特征学习和端到端检测的能力，与基于传统视觉的检测方法相比，泛化能力有较大的提升，能够有效识别常规长宽比缺陷，显著地提升了工业自动化水平和生产效率。然而，该类方法在条状缺陷检测方面表现欠佳，如何提高基于卷积神经网络检测方法在极端长宽比情况下的检测性能还有待进一步的研究。1.2条状表面缺陷检测研究现状TC"1.2StatusofResearchonSliverSurfaceDefectDetection"\l3为提高极端长宽比缺陷的检测性能，基于卷积神经网络的检测方法多采用K-means或其改进方法ADDINEN.CITE<EndNote><Cite><Author>Arthur</Author><Year>2006</Year><RecNum>128</RecNum><DisplayText><styleface="superscript">[33,34]</style></DisplayText><record><rec-number>128</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1642950899">128</key></foreign-keys><ref-typename="Report">27</ref-type><contributors><authors><author>Arthur,David</author><author>Vassilvitskii,Sergei</author></authors></contributors><titles><title>k-means++:Theadvantagesofcarefulseeding</title></titles><dates><year>2006</year></dates><publisher>Stanford</publisher><urls></urls></record></Cite><Cite><Author>Bahmani</Author><Year>2012</Year><RecNum>203</RecNum><record><rec-number>203</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646384232">203</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Bahmani,Bahman</author><author>Moseley,Benjamin</author><author>Vattani,Andrea</author><author>Kumar,Ravi</author><author>Vassilvitskii,Sergei</author></authors></contributors><titles><title>Scalablek-means++</title><secondary-title>arXivpreprintarXiv:1203.6402</secondary-title></titles><periodical><full-title>arXivpreprintarXiv:1203.6402</full-title></periodical><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>[33,34]对缺陷数据集的锚框重新聚类，以获得更符合缺陷形状特征的锚框，但此类方法对数据集的要求比较高，一般对规模较大且目标尺度跨度小的数据集有效，在小数据集、目标尺度变换幅度大的情况下效果有限。Peng等人提出基于FasterR-CNN的改进网络PrioriAnchorConvolutionalNeuralNetwork（PRAN-Net），作者认为，FasterR-CNN的子网络RPN（Regionproposalnetwork）采用固定的锚框来指导候选框的生成，这一思路并不适合极端长宽比缺陷ADDINEN.CITE<EndNote><Cite><Author>Peng</Author><Year>2020</Year><RecNum>202</RecNum><DisplayText><styleface="superscript">[35]</style></DisplayText><record><rec-number>202</rec-number><foreign-keys><keyapp="EN"db-id="9atptfrtf25rebe05vrprz0qx0dzzt2s0dpf"timestamp="1646383938">202</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Peng,Peiran</author><author>Wang,Ying</author><author>Hao,Can</author><author>Zhu,Zhizhong</author><author>Liu,Tong</author><author>Zhou,Weihu</author></authors></contributors><titles><title>Auto