基于计算机视觉的电表PCB板智能识别系统的研究
|
摘要
印刷电路板(PCB, Printed Circuit Board)是集成各种电子元器件的信息载体,在各个领域得到了广泛的应用,并且伴随着生产技术的不断提高,PCB板朝着多层数,高密度的方向发展。由于PCB制作工艺日趋复杂,使PCB的质量检验成为一件非常困难的工作。传统的检测方法已不能满足生产的需要。基于计算机与图像处理技术进行的PCB自动视觉检测的研究开始成为PCB检测的热门方向。
本文主要研究利用目前工业中应用最多的BP (BP,Back Propogation)神经网络和AOI(Automatic Optic Inspection,自动光学检测)技术,借助计算机视觉,根据电表PCB的元件特征,进行PCB板图像的匹配识别。本文主要就以下几个问题进行了具体研究
首先介绍了研究自动检测PCB板对于行业的重要性及采用的技术手段,PCB板图像识别的基本思想和技术特点；随后,阐述了PCB板元件图像的特征提取方式,介绍了图像的处理相关的算法；然后,本文详细描述了神经网络在该系统中的实现和技术特点。设计和制作了相应的检测设备,编写了配套的操作软件用于采集图像,匹配识别,分析结论,给出报表。实验结果表明本方法误判率低、匹配精度高,可以应用于PCB板中元件的一致性检测。
Print Circuit Board (PCB) is an information carrier integrating various electronic components. It has been applied in different fields widely.also with the continuous development of production technology, PCB are prone to be more layers and more density。Because of the complexity of PCB production process, makes the PCB quality inspection become a very difficult job. The traditional examination method can not satisfy the production's needs already. The study on the automatic vision examination of PCB flaw is popular in the PCB examination, which is based on the computer and the image processing technology.
This paper mainly research on the application of BP(Back Propogation) neural network and AOI (Automatic Optic Inspection) Technology which most widely used in industry, with the help of computer vision, according to characteristics of the meter PCB components, carrying out the matching recognition of the PCB's image. This review focuses on the following specific research issues.
First, it is introduced the importance of Automatic detection of PCB board and the technical means used, the basic idea and technical features of PCB board image recognition; next, it is illustrated the extraction method of the PCB board components image's feature, and introduced the related algorithms for image processing; then,this paper described the implementation and technical features of neural network in this systems in detailed. We have designed and produced the corresponding detection device, prepared the supporting operating software for image acquisition, matching recognition, result analysis, reports given. The experimental results indicates that this means have low error decision rate, high matching precision, can be used in testing the consistency of PCB board's components.
本文主要研究利用目前工业中应用最多的BP (BP,Back Propogation)神经网络和AOI(Automatic Optic Inspection,自动光学检测)技术,借助计算机视觉,根据电表PCB的元件特征,进行PCB板图像的匹配识别。本文主要就以下几个问题进行了具体研究
首先介绍了研究自动检测PCB板对于行业的重要性及采用的技术手段,PCB板图像识别的基本思想和技术特点；随后,阐述了PCB板元件图像的特征提取方式,介绍了图像的处理相关的算法；然后,本文详细描述了神经网络在该系统中的实现和技术特点。设计和制作了相应的检测设备,编写了配套的操作软件用于采集图像,匹配识别,分析结论,给出报表。实验结果表明本方法误判率低、匹配精度高,可以应用于PCB板中元件的一致性检测。
Print Circuit Board (PCB) is an information carrier integrating various electronic components. It has been applied in different fields widely.also with the continuous development of production technology, PCB are prone to be more layers and more density。Because of the complexity of PCB production process, makes the PCB quality inspection become a very difficult job. The traditional examination method can not satisfy the production's needs already. The study on the automatic vision examination of PCB flaw is popular in the PCB examination, which is based on the computer and the image processing technology.
This paper mainly research on the application of BP(Back Propogation) neural network and AOI (Automatic Optic Inspection) Technology which most widely used in industry, with the help of computer vision, according to characteristics of the meter PCB components, carrying out the matching recognition of the PCB's image. This review focuses on the following specific research issues.
First, it is introduced the importance of Automatic detection of PCB board and the technical means used, the basic idea and technical features of PCB board image recognition; next, it is illustrated the extraction method of the PCB board components image's feature, and introduced the related algorithms for image processing; then,this paper described the implementation and technical features of neural network in this systems in detailed. We have designed and produced the corresponding detection device, prepared the supporting operating software for image acquisition, matching recognition, result analysis, reports given. The experimental results indicates that this means have low error decision rate, high matching precision, can be used in testing the consistency of PCB board's components.
引文
[1]范小涛,夏雨人.一种基于图像处理的PCB检测算法研究[J].计算机工程与应用,2004,56(13)：51-53.
[2]高湘铃.PCB数字图像检测与识别研究[D].西南交通大学,2003：21-23.
[3]张立等.PCB板检测中的定位研究[D].中国图像图形学报,999(8).
[4]姚立新,张武学,连军莉.AOI系统在PCB中的应用[J].电子工业专用设备.2004, (5)：25-28.
[5]谢佳豪.基于计算机视觉的印刷电路板自动检测系统的研究[D].西安理工大学硕士论文,2004,50-51.
[6]朱淼良,计算机视觉[M].浙江大学出版社,1997.
[7]马颂德,张正友.计算机视觉[M].科学出版社,1998.
[8]贾云得.机器视觉[M].科学出版社,2000.
[9]David. A, Forsyth, Jean Ponce.计算机视觉(美)[M].清华大学出版社,2004.
[10]刘榴娣,刘明奇.实用数字图像处理[M].北京理工大学出版社,1998.
[11]崔屹.数字图像处理技术与应用[M].电子工业出版社,1997.
[12]赵荣椿.数字图像处理导论[M].西北工业大学出版社,1995.
[13]黄贤武,王加俊.数字图像处理与压缩编码技术[M].电子科技大学出版社,2000.
[14]傅德胜,寿益禾.图形图像处理学[M].东南大学出版社,2002.
[15]夏良正.数字图像处理[M].东南大学出版社,1999.
[16]Kenneth R.Castleman. Digital image processing[M].清华大学出版社,2003.
[17]Rafael C. Gonzalez. Richard E. Woods. Digital image processing. Publishing House of Electronics Industry,2002.
[18]冈萨雷斯(李叔梁等译).数字图像处理[M].科学出版社,1983.
[19]Milan Sonka, Vaclav Hlavac, Roger Boyle. Image processing analysis and machine vision[M].人民邮电出版社,2002.
[20]孙即祥.图像分析[M].科学出版社,2005.
[21]G.S.Cox. Template matching ane measure of match in image processing[M].科学出版社,2005.
[22]于亦凡,周祥龙.模糊图像的Kalman滤波恢复方法[J].青岛大学学报,2001.
[23]郑南宁.计算机视觉与模式识别[M].国防工业出版社,1998.
[24]阮秋琦.数字图像处理学[M].电子工业出版社,2001.
[25]Steven M, Kay. Fundamentals of Statistical Signal Processing-Estimation Theory and Detection Theory[J]. Publishing House of Electronics Industry,2003.8.
[26]邓自立.最优滤波理论及其应用[C].哈尔滨工业大学出版社,2000.
[27]邓自立.卡尔曼滤波与维纳滤波[M].哈尔滨工业大学出版社,2001.
[28]龚耀寰.自适应滤波-时域自适应滤波和智能天线[M].电子工业出版社,2003.
[29]Bo-CaiGao. An Operational Method for Estimating Signal to Noise Ratios from
Data Acquired with Imaging Spectrometers[J]. Remote Sensing Environment,1993, 43:23-33.
[30]张大鹏.模式识别与图像处理并行计算机系统设计(香港)[M].哈尔滨工业大学出版社,1998.
[31]章毓晋.图像工程[M].清华大学出版社,2000.
[32]Bob Reselman,Richard Peasley[美].实用Visual Basic 6教程[M].清华大学出版社,2001.
[33]张葛祥,李娜.MATLAB仿真技术与应用[M].清华大学出版社,2003.
[34]余成波.数字图像处理及MATLAB实现[M].重庆大学出版社.2003
[35]Trahanias P E, Venetsanopoulos A N. Vector order statistics operators as color edge detectors [J]. IEEE, Transactions on Systems,Man and Cybernetics,1996, 135-143.
[36]Carevic D. Region-based coding of color image using Karhunen-Loeve Transform [J]. Graphics Models and Image Processing,1997,59(1):27-38.
[37]Wang X W. The focus segmentation of color ophthalm ologic image based on modified K-means clustering and mathematical morphology [J]. Chinese Jounal of Biomedical Enginerring,2002,21(5):443-448.
[38]Bezdec J C. patterm recognition with fuzzy objective function algorithms[M]. New York Plenum Press,1981.
[39]Pham. Image Segmentation using Probabilistic Fuzzy C-Means Clustering[J]. Proceedings of International Conference on Image processing,2001, 21(3)):722-725.
[40]Nobuyuki Otsu. A Threshold Selection Method from Gray Level Histograms [J]. IEEE Transactionss, man and cyberneticss,1979s,9(1).
[41]Conzalez R C, Woods R E. Digital Image Processing[M].3rd, Addison Wesley ublishing Company,1992.
[42]周长发.精通Visual C++图像编程[M].北京,科学出版社,2004,56-60.
[43]龚声蓉,熊璋等,一种基于HSL概率密度的彩色图象分割方法[J].小微型计算机系统,2001,22(6)：695-698.
[44]李丽.基于主特征量自适应选择的彩色图像分割方法研究[D].沈阳：辽宁师范大学硕士论文,2005.
[45]Yasuhiko hara. A System for PCB Automated Inspection Using Fluorescent Light. IEEE Transaction Analysis and Machine Intelligence,1998,10(1):101-104.
[46]A.P.Sprague. A Method for Automatic Inspection of Printed Circuit Boards[M]. Image Understanding,2001,54(3):401-415.
[47]叶齐祥,高文,王伟强.一种融合颜色和空间信息的彩色图像分割算法[J].软件学报,2004,15(4)：522-530.
[48]J.C.Mott-Smith. Medial Axis Transforms Picture Processing and Psychopictorics[J]. Academic Press,New York,1970.
[49]C. Arcelli and G. Sanniti Di Baja. On the Sequential Approach to Medial Line Thinning Transformation[J]. IEEE Trans. Systems,Man,and Cybernetics, SMC-8(2) 1978:139-144.
[50]H.Blum. Biological Science and VisualShape[J]. J.Theor.Biol.1973,38(3): 205-287.
[51]章毓晋.图像工程[M].清华大学出版社,2002,246-249.
[52]何斌,马天予等.Visual C++数字图像处理[M].北京人民邮电出版社,2001：152-160.
[53]罗军辉,冯平,哈力旦.A.MATLAT 7.0在图像处理中的应用[M].北京：机械工业出版社,2006.2-3.
[54]胡跃明,潭颖.自动光学检测在中国的应用现状和发展[J].微计算机信息,2006(4),
[55]周煜.如何对表面贴装印刷电路板进行缺陷检查[J].湖北广播电视大学学报,2007,(6).
[56]蔡积庆.PCB缺陷检查中的AOI/AFI[J].印制电路信息,2002,50-52.
[57]鲜飞.AOI技术在SMT生产中的实施技巧与才略[J].印制电路信息,2003,56-57.
[58]席砺莼,闫宏伟.彩色图像的分割技术[J].微机发展,2003,46-48.
[59]叶齐祥,高文,王伟强,等.一种融合颜色和空间信息的彩色图像分割算法[J].软件学报,2004,(2)：522-530.
[60]章毓晋.图像工程[M].北京,清华大学出版社,2000.
[2]高湘铃.PCB数字图像检测与识别研究[D].西南交通大学,2003：21-23.
[3]张立等.PCB板检测中的定位研究[D].中国图像图形学报,999(8).
[4]姚立新,张武学,连军莉.AOI系统在PCB中的应用[J].电子工业专用设备.2004, (5)：25-28.
[5]谢佳豪.基于计算机视觉的印刷电路板自动检测系统的研究[D].西安理工大学硕士论文,2004,50-51.
[6]朱淼良,计算机视觉[M].浙江大学出版社,1997.
[7]马颂德,张正友.计算机视觉[M].科学出版社,1998.
[8]贾云得.机器视觉[M].科学出版社,2000.
[9]David. A, Forsyth, Jean Ponce.计算机视觉(美)[M].清华大学出版社,2004.
[10]刘榴娣,刘明奇.实用数字图像处理[M].北京理工大学出版社,1998.
[11]崔屹.数字图像处理技术与应用[M].电子工业出版社,1997.
[12]赵荣椿.数字图像处理导论[M].西北工业大学出版社,1995.
[13]黄贤武,王加俊.数字图像处理与压缩编码技术[M].电子科技大学出版社,2000.
[14]傅德胜,寿益禾.图形图像处理学[M].东南大学出版社,2002.
[15]夏良正.数字图像处理[M].东南大学出版社,1999.
[16]Kenneth R.Castleman. Digital image processing[M].清华大学出版社,2003.
[17]Rafael C. Gonzalez. Richard E. Woods. Digital image processing. Publishing House of Electronics Industry,2002.
[18]冈萨雷斯(李叔梁等译).数字图像处理[M].科学出版社,1983.
[19]Milan Sonka, Vaclav Hlavac, Roger Boyle. Image processing analysis and machine vision[M].人民邮电出版社,2002.
[20]孙即祥.图像分析[M].科学出版社,2005.
[21]G.S.Cox. Template matching ane measure of match in image processing[M].科学出版社,2005.
[22]于亦凡,周祥龙.模糊图像的Kalman滤波恢复方法[J].青岛大学学报,2001.
[23]郑南宁.计算机视觉与模式识别[M].国防工业出版社,1998.
[24]阮秋琦.数字图像处理学[M].电子工业出版社,2001.
[25]Steven M, Kay. Fundamentals of Statistical Signal Processing-Estimation Theory and Detection Theory[J]. Publishing House of Electronics Industry,2003.8.
[26]邓自立.最优滤波理论及其应用[C].哈尔滨工业大学出版社,2000.
[27]邓自立.卡尔曼滤波与维纳滤波[M].哈尔滨工业大学出版社,2001.
[28]龚耀寰.自适应滤波-时域自适应滤波和智能天线[M].电子工业出版社,2003.
[29]Bo-CaiGao. An Operational Method for Estimating Signal to Noise Ratios from
Data Acquired with Imaging Spectrometers[J]. Remote Sensing Environment,1993, 43:23-33.
[30]张大鹏.模式识别与图像处理并行计算机系统设计(香港)[M].哈尔滨工业大学出版社,1998.
[31]章毓晋.图像工程[M].清华大学出版社,2000.
[32]Bob Reselman,Richard Peasley[美].实用Visual Basic 6教程[M].清华大学出版社,2001.
[33]张葛祥,李娜.MATLAB仿真技术与应用[M].清华大学出版社,2003.
[34]余成波.数字图像处理及MATLAB实现[M].重庆大学出版社.2003
[35]Trahanias P E, Venetsanopoulos A N. Vector order statistics operators as color edge detectors [J]. IEEE, Transactions on Systems,Man and Cybernetics,1996, 135-143.
[36]Carevic D. Region-based coding of color image using Karhunen-Loeve Transform [J]. Graphics Models and Image Processing,1997,59(1):27-38.
[37]Wang X W. The focus segmentation of color ophthalm ologic image based on modified K-means clustering and mathematical morphology [J]. Chinese Jounal of Biomedical Enginerring,2002,21(5):443-448.
[38]Bezdec J C. patterm recognition with fuzzy objective function algorithms[M]. New York Plenum Press,1981.
[39]Pham. Image Segmentation using Probabilistic Fuzzy C-Means Clustering[J]. Proceedings of International Conference on Image processing,2001, 21(3)):722-725.
[40]Nobuyuki Otsu. A Threshold Selection Method from Gray Level Histograms [J]. IEEE Transactionss, man and cyberneticss,1979s,9(1).
[41]Conzalez R C, Woods R E. Digital Image Processing[M].3rd, Addison Wesley ublishing Company,1992.
[42]周长发.精通Visual C++图像编程[M].北京,科学出版社,2004,56-60.
[43]龚声蓉,熊璋等,一种基于HSL概率密度的彩色图象分割方法[J].小微型计算机系统,2001,22(6)：695-698.
[44]李丽.基于主特征量自适应选择的彩色图像分割方法研究[D].沈阳：辽宁师范大学硕士论文,2005.
[45]Yasuhiko hara. A System for PCB Automated Inspection Using Fluorescent Light. IEEE Transaction Analysis and Machine Intelligence,1998,10(1):101-104.
[46]A.P.Sprague. A Method for Automatic Inspection of Printed Circuit Boards[M]. Image Understanding,2001,54(3):401-415.
[47]叶齐祥,高文,王伟强.一种融合颜色和空间信息的彩色图像分割算法[J].软件学报,2004,15(4)：522-530.
[48]J.C.Mott-Smith. Medial Axis Transforms Picture Processing and Psychopictorics[J]. Academic Press,New York,1970.
[49]C. Arcelli and G. Sanniti Di Baja. On the Sequential Approach to Medial Line Thinning Transformation[J]. IEEE Trans. Systems,Man,and Cybernetics, SMC-8(2) 1978:139-144.
[50]H.Blum. Biological Science and VisualShape[J]. J.Theor.Biol.1973,38(3): 205-287.
[51]章毓晋.图像工程[M].清华大学出版社,2002,246-249.
[52]何斌,马天予等.Visual C++数字图像处理[M].北京人民邮电出版社,2001：152-160.
[53]罗军辉,冯平,哈力旦.A.MATLAT 7.0在图像处理中的应用[M].北京：机械工业出版社,2006.2-3.
[54]胡跃明,潭颖.自动光学检测在中国的应用现状和发展[J].微计算机信息,2006(4),
[55]周煜.如何对表面贴装印刷电路板进行缺陷检查[J].湖北广播电视大学学报,2007,(6).
[56]蔡积庆.PCB缺陷检查中的AOI/AFI[J].印制电路信息,2002,50-52.
[57]鲜飞.AOI技术在SMT生产中的实施技巧与才略[J].印制电路信息,2003,56-57.
[58]席砺莼,闫宏伟.彩色图像的分割技术[J].微机发展,2003,46-48.
[59]叶齐祥,高文,王伟强,等.一种融合颜色和空间信息的彩色图像分割算法[J].软件学报,2004,(2)：522-530.
[60]章毓晋.图像工程[M].北京,清华大学出版社,2000.