木工锯片锯齿综合尺寸检测系统的研究
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摘要
光电检测技术是近年来在测量领域中一种新兴的检测技术,它以光学技术为基础,利用数字图像处理技术对光学信息进行处理。光电检测技术把光电子学、激光、计算机技术等多种先进的科学技术相结合,构成光、机、电、算相融合的检测系统。本论文研究的木工锯片锯齿综合尺寸检测系统主要由照明系统、光接收系统、图像传感系统、图像采集系统、图像处理系统、计算机及其外设几个部分组成。这种系统是一种非接触的检测系统,具有检测速度快、检测对象范围宽、检测精度高、测量结果直观等特点。本系统的实践证明了基于数字图像处理的光电检测技术在尺寸检测中效果非常好。
本课题主要研究外径等于或小于420mm木工锯片基体几何量的测试。待测几何量包括:锯齿齿距、前角、后角及齿型相关尺寸。木工锯片锯齿的综合尺寸的测量精确度要求控制在微米量级。传统的手动测量方法,很难控测量精度,而且测量结果不直观、数据整理缓慢。为满足实际检测的高精度的要求,本课题采用CMOS传感器检测锯片图像,首先采集木工锯片锯齿的灰度图像,再对其进行阈值分割、图像噪声滤波、提取边缘、Hough变换、曲线跟踪、角点检测等一系列数字图像处理,最后得到锯片锯齿的综合尺寸,该检测系统采用非接触式测量,而且可以达到高精度测量的目标。
本论文共分为五章,第一章为绪论,介绍了课题来源和背景、国内外图像测量技术的发展和趋势以及课题研究的主要内容和重点;第二章叙述了图像检测原理和检测方案的设计;第三章详述了数字图像处理的算法和实验过程;第四章对实验结果进行了分析;第五章是结论部分,对全文进行总结和展望。
In recent years, In recent years, photoelectric detection is an emerging high- performance measurement technique in the field of measurement, it is based on optical technology, and use digital image processing technology to process optical information. Photoelectric detection integrates advanced science and technologies such as optoelectronics, laser technology, computer technology as a whole, constitute a complex measurement system of light, machines, electricity and computer. In this paper, the researched woodworking synthetical size measurement system consists of lighting system, optical receiving system, image sensor systems, image acquisition systems, image processing systems, computer parts and peripherals. This is a non-contact detection system with the advantages of high detection speed, wide detection range, high detection precision, intuitionistic measurement results and others. The practice has proved that the photoelectric detection technology based on digital image processing has a very good effect in size detecting.
This research focuses on measuring matrix geometric volume of saw woodworking whose outside diameter is equal to or less than 420mm. Geometric volumes to test include: saw tooth pitch, anterior horn, posterior horn and related tooth-type size. The precision of Woodworking Saw measurements are required to control at micron scale. Traditional manual measurement methods are difficult to control the measurement accuracy, and the measurement results are not intuitive, data management is slow. To meet the requirements of high-precision in practical detection, This research uses CMOS image sensors to detect Woodworking Saw image, then use a series of digital image processing such as gray-scale conversion, threshold segmentation, image noise filtering, edge extraction, Hough transform, curve tracking, corner detection to process the collected picture, and finally get the general size of the Woodworking Saw. The detection system uses the way of non-contact measurement, and can achieve the goal of high-precision measurements.
The thesis is divided into five chapters, the first chapter is the preface, which introduces the origin and background of the subject, domestic and international image measurement technology developments and trends, as well as the main contents and focus of the research; Chapter II describes the principle of image detection and design of detecting scheme; Chapter III discusses the digital image processing algorithms and the implementation process in detail; The fourth chapter analyze the experimental results; The fifth chapter is the concluding part, in which the summary and outlook are spread out.
本课题主要研究外径等于或小于420mm木工锯片基体几何量的测试。待测几何量包括:锯齿齿距、前角、后角及齿型相关尺寸。木工锯片锯齿的综合尺寸的测量精确度要求控制在微米量级。传统的手动测量方法,很难控测量精度,而且测量结果不直观、数据整理缓慢。为满足实际检测的高精度的要求,本课题采用CMOS传感器检测锯片图像,首先采集木工锯片锯齿的灰度图像,再对其进行阈值分割、图像噪声滤波、提取边缘、Hough变换、曲线跟踪、角点检测等一系列数字图像处理,最后得到锯片锯齿的综合尺寸,该检测系统采用非接触式测量,而且可以达到高精度测量的目标。
本论文共分为五章,第一章为绪论,介绍了课题来源和背景、国内外图像测量技术的发展和趋势以及课题研究的主要内容和重点;第二章叙述了图像检测原理和检测方案的设计;第三章详述了数字图像处理的算法和实验过程;第四章对实验结果进行了分析;第五章是结论部分,对全文进行总结和展望。
In recent years, In recent years, photoelectric detection is an emerging high- performance measurement technique in the field of measurement, it is based on optical technology, and use digital image processing technology to process optical information. Photoelectric detection integrates advanced science and technologies such as optoelectronics, laser technology, computer technology as a whole, constitute a complex measurement system of light, machines, electricity and computer. In this paper, the researched woodworking synthetical size measurement system consists of lighting system, optical receiving system, image sensor systems, image acquisition systems, image processing systems, computer parts and peripherals. This is a non-contact detection system with the advantages of high detection speed, wide detection range, high detection precision, intuitionistic measurement results and others. The practice has proved that the photoelectric detection technology based on digital image processing has a very good effect in size detecting.
This research focuses on measuring matrix geometric volume of saw woodworking whose outside diameter is equal to or less than 420mm. Geometric volumes to test include: saw tooth pitch, anterior horn, posterior horn and related tooth-type size. The precision of Woodworking Saw measurements are required to control at micron scale. Traditional manual measurement methods are difficult to control the measurement accuracy, and the measurement results are not intuitive, data management is slow. To meet the requirements of high-precision in practical detection, This research uses CMOS image sensors to detect Woodworking Saw image, then use a series of digital image processing such as gray-scale conversion, threshold segmentation, image noise filtering, edge extraction, Hough transform, curve tracking, corner detection to process the collected picture, and finally get the general size of the Woodworking Saw. The detection system uses the way of non-contact measurement, and can achieve the goal of high-precision measurements.
The thesis is divided into five chapters, the first chapter is the preface, which introduces the origin and background of the subject, domestic and international image measurement technology developments and trends, as well as the main contents and focus of the research; Chapter II describes the principle of image detection and design of detecting scheme; Chapter III discusses the digital image processing algorithms and the implementation process in detail; The fourth chapter analyze the experimental results; The fifth chapter is the concluding part, in which the summary and outlook are spread out.
引文
[1]贺霖.图像测量及分析系统的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2003
[2]徐国盛.基于CCD扫描的缺陷检测数字化技术与边缘提取技术: [硕士学位论文].武汉:华中科技大学图书馆, 2006
[3]魏钰.基于数字图像轮廓特征的机械零件识别: [硕士学位论文].武汉:华中科技大学图书馆, 2004
[4]丁金明.金属镀层工件表面缺陷自动检测系统的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2004
[5]李丽娟.目标激光散射特性在钢板表面微观轮廓精度测量中的应用: [博士学位论文].武汉:华中科技大学图书馆, 2005
[6]李坤全.人体图像边缘检测算法的研究及实现: [硕士学位论文].武汉:华中科技大学图书馆, 2003
[7]卓红俞.手推式钢轨外形尺寸图像检测系统: [硕士学位论文].武汉:华中科技大学图书馆, 2005
[8]赵晶莹.图像处理中的算法分析: [硕士学位论文].武汉:华中科技大学图书馆, 2002
[9]程俊鑫.基于灰度图像的边缘检测方法研究: [硕士学位论文].武汉:华中科技大学图书馆, 2007
[10]罗洪斌.数字图像处理技术在结构检测中的应用: [硕士学位论文].武汉:华中科技大学图书馆, 2006
[11]盛道清.图像增强算法的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2007
[12]纪万鹏.图像测量技术在工业测量中的应用及展望.沈阳机电学院学报, 1985, 7(1): 117～125
[13]李庆利,张立萍,钱丹青等.机械零件尺寸图像测量中的边缘检测算法.机械设计与制造, 2005, (8): 64～65
[14]李勇,付小莉.基于小波变换的人体图像边缘检测.中原工学院学报, 2002, 13(3): 5～6
[15]张长江,付梦印,金梅.基于模拟退火算法的红外图像自适应对比度增强.中国图像图形学报, 2004, 9(4): 391～395
[16]谷红勋,钟春香,刘华方.基于子形心集Hausdorff距离的平面形状识别新方法.中国图像图形学报, 1997, 2(3): 149～152
[17]黄红艳,杨煜普.基于高阶神经网络的机械零件形状识别.上海交通大学学报. 2001, 35(8): 1144～1147
[18] K.R.Castleman.数字图像处理.朱志刚,石定机.北京:电子工业出版社, 1998. 124～158
[19]张立科. Visual C++数字图像处理典型算法及实现. (第一版).北京:人民邮电出版社, 2006. 429～478
[20]孙兆林. MATLAB 6.x图像处理. (第一版).北京:清华大学出版社, 2002. 243～247
[21] L G Roberts. Perception Three-Dimensional Solids. Optical and Electro-Optical Information Processing, 1965. 59～97
[22]易玲.基于分级的快速霍夫变换直线检测.微计算机信息, 2007, 23(11-1): 206～208
[23]侯北平,李平,宋执环.基于滑动窗口的自适应角点检测研究.电路与系统学报, 2006, 11(6): 133～137
[24]陈向伟.机械零件计算机视觉检测关键技术的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2005
[25]冯铁成.基于数字图像处理的刚才测数方法的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2008
[26] Ballard D H. Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition, 1981, 13(2): 111～122
[27]王虹,潘晓露,李一民等.一种基于梯度幅度值的图像质量客观评价方法.光学与光电技术, 2004, 2(6): 23～25
[28] Ching Y T. Detecting Line Segments in an Image——a New Implementation for Hough Transform. Patern Recognition Letters, 2000, 2(3): 421～429
[29] Illingworth J, Kittler J. A Survey of the Hough Transform. CVGIP, 1988, 44: 87～116
[30] Princen J, Illing worth J, Kittler J. A Formal Definition of the Hough Transform Properties and Relationships. Mathematical Image and Vision, 1992, 5(1): 148～153
[31]张祖勋,张剑清.数字摄影测量学.武汉:武汉大学出版社, 2002. 121～131
[32]王亮,卜佳俊.参数控制的边缘直线提取算法.计算机工程. 2004, 30(1): 148～149
[33] Shpitalni M, Koren Y, Lo C C. Realtime curve interpolators. Computeraided Design, 1994, 26(11): 832～838
[34] Cheng M Y, Tsai M C, Kuo J C. Realtime nurbs command generators for CNC servo controllers. Machining Tools & Manufac2ture, 2002, 42: 801～813
[35]施法中.计算机辅助几何设计与非均匀有理B样条.北京:高等教育出版社, 2001. 211～248
[36]宋瑞霞,王小春.带参数的多结点样条.计算机辅助设计与图形学学报, 2003, 15(11): 1422～1427
[37]齐东旭.多结点样条插值曲线与曲面的矩阵表达及余项估计.计算数学, 1982, 4 (3): 244～252
[38] A Rosenfeld, J S Weszka. An improved method of angle detection on digital curves. IEEE Trans. Computers, 1975, 24: 940～941
[39]肖茜,鲁宏伟.基于高斯平滑的自适应角点检测.计算机辅助设计与图形学学报, 2003, 15(11): 1358～1361
[40] H L Beus, S H Tiu. An improved corner detection algorithm based on chain-coded plane curves. Pattern Recognition, 1987, 20: 291～296
[41] H Freeman, L S Davis. A corner-finding algorithm for chain code curves. IEEE Trans. Comput, 1977, C-26: 297～303
[42] F Mokhtarian, A Mackworth. Scale-based description and recognition of planar curvesand two-dimentional shapes. IEEE Trans. Pattern Recognition and Machine Intelligence. 1986, 8(1): 34～43
[43] Shigeru Ando. Image field categorization and edge/corner detection from gradient covariance. IEEE Transactions on Pattern Analysisand Machine Intelligence, 2000, 22(2): 179～190
[44]王建中,姜昱明.基于Snake模型的视频对象分割和跟踪算法.计算机工程与设计, 2005, 26(7): 1928～1930
[45]姜玲玉. NURBS曲线曲率连续延拓的一种有效算法.计算机工程与设计, 2004, 25(12): 2340～2342
[46] Gene C H Chuang, C C Jay Kuo. Wavelet descriptor of planar curves theory and applications. IEEE Trans. Image Processing, 1996, 5(1): 56～70
[47] Y P Wang, S L Lee. Multiscale curvature-based shape representation using B-spline wavelets. IEEE Trans. Image Processing, 1999, 8(11): 1586～1592
[48]王博,潘泉,张洪才等.图像平滑与边缘检测的模糊向量描述.小型微型计算机系统, 1999, 20(3): 218～221
[49] Canny John. A Computational Approach to Edge Detection. IEEE Transactions on PatternAnalysis and Machine Intelligence, 1986, PAMI-8(6): 679～698
[2]徐国盛.基于CCD扫描的缺陷检测数字化技术与边缘提取技术: [硕士学位论文].武汉:华中科技大学图书馆, 2006
[3]魏钰.基于数字图像轮廓特征的机械零件识别: [硕士学位论文].武汉:华中科技大学图书馆, 2004
[4]丁金明.金属镀层工件表面缺陷自动检测系统的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2004
[5]李丽娟.目标激光散射特性在钢板表面微观轮廓精度测量中的应用: [博士学位论文].武汉:华中科技大学图书馆, 2005
[6]李坤全.人体图像边缘检测算法的研究及实现: [硕士学位论文].武汉:华中科技大学图书馆, 2003
[7]卓红俞.手推式钢轨外形尺寸图像检测系统: [硕士学位论文].武汉:华中科技大学图书馆, 2005
[8]赵晶莹.图像处理中的算法分析: [硕士学位论文].武汉:华中科技大学图书馆, 2002
[9]程俊鑫.基于灰度图像的边缘检测方法研究: [硕士学位论文].武汉:华中科技大学图书馆, 2007
[10]罗洪斌.数字图像处理技术在结构检测中的应用: [硕士学位论文].武汉:华中科技大学图书馆, 2006
[11]盛道清.图像增强算法的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2007
[12]纪万鹏.图像测量技术在工业测量中的应用及展望.沈阳机电学院学报, 1985, 7(1): 117～125
[13]李庆利,张立萍,钱丹青等.机械零件尺寸图像测量中的边缘检测算法.机械设计与制造, 2005, (8): 64～65
[14]李勇,付小莉.基于小波变换的人体图像边缘检测.中原工学院学报, 2002, 13(3): 5～6
[15]张长江,付梦印,金梅.基于模拟退火算法的红外图像自适应对比度增强.中国图像图形学报, 2004, 9(4): 391～395
[16]谷红勋,钟春香,刘华方.基于子形心集Hausdorff距离的平面形状识别新方法.中国图像图形学报, 1997, 2(3): 149～152
[17]黄红艳,杨煜普.基于高阶神经网络的机械零件形状识别.上海交通大学学报. 2001, 35(8): 1144～1147
[18] K.R.Castleman.数字图像处理.朱志刚,石定机.北京:电子工业出版社, 1998. 124～158
[19]张立科. Visual C++数字图像处理典型算法及实现. (第一版).北京:人民邮电出版社, 2006. 429～478
[20]孙兆林. MATLAB 6.x图像处理. (第一版).北京:清华大学出版社, 2002. 243～247
[21] L G Roberts. Perception Three-Dimensional Solids. Optical and Electro-Optical Information Processing, 1965. 59～97
[22]易玲.基于分级的快速霍夫变换直线检测.微计算机信息, 2007, 23(11-1): 206～208
[23]侯北平,李平,宋执环.基于滑动窗口的自适应角点检测研究.电路与系统学报, 2006, 11(6): 133～137
[24]陈向伟.机械零件计算机视觉检测关键技术的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2005
[25]冯铁成.基于数字图像处理的刚才测数方法的研究: [硕士学位论文].武汉:华中科技大学图书馆, 2008
[26] Ballard D H. Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition, 1981, 13(2): 111～122
[27]王虹,潘晓露,李一民等.一种基于梯度幅度值的图像质量客观评价方法.光学与光电技术, 2004, 2(6): 23～25
[28] Ching Y T. Detecting Line Segments in an Image——a New Implementation for Hough Transform. Patern Recognition Letters, 2000, 2(3): 421～429
[29] Illingworth J, Kittler J. A Survey of the Hough Transform. CVGIP, 1988, 44: 87～116
[30] Princen J, Illing worth J, Kittler J. A Formal Definition of the Hough Transform Properties and Relationships. Mathematical Image and Vision, 1992, 5(1): 148～153
[31]张祖勋,张剑清.数字摄影测量学.武汉:武汉大学出版社, 2002. 121～131
[32]王亮,卜佳俊.参数控制的边缘直线提取算法.计算机工程. 2004, 30(1): 148～149
[33] Shpitalni M, Koren Y, Lo C C. Realtime curve interpolators. Computeraided Design, 1994, 26(11): 832～838
[34] Cheng M Y, Tsai M C, Kuo J C. Realtime nurbs command generators for CNC servo controllers. Machining Tools & Manufac2ture, 2002, 42: 801～813
[35]施法中.计算机辅助几何设计与非均匀有理B样条.北京:高等教育出版社, 2001. 211～248
[36]宋瑞霞,王小春.带参数的多结点样条.计算机辅助设计与图形学学报, 2003, 15(11): 1422～1427
[37]齐东旭.多结点样条插值曲线与曲面的矩阵表达及余项估计.计算数学, 1982, 4 (3): 244～252
[38] A Rosenfeld, J S Weszka. An improved method of angle detection on digital curves. IEEE Trans. Computers, 1975, 24: 940～941
[39]肖茜,鲁宏伟.基于高斯平滑的自适应角点检测.计算机辅助设计与图形学学报, 2003, 15(11): 1358～1361
[40] H L Beus, S H Tiu. An improved corner detection algorithm based on chain-coded plane curves. Pattern Recognition, 1987, 20: 291～296
[41] H Freeman, L S Davis. A corner-finding algorithm for chain code curves. IEEE Trans. Comput, 1977, C-26: 297～303
[42] F Mokhtarian, A Mackworth. Scale-based description and recognition of planar curvesand two-dimentional shapes. IEEE Trans. Pattern Recognition and Machine Intelligence. 1986, 8(1): 34～43
[43] Shigeru Ando. Image field categorization and edge/corner detection from gradient covariance. IEEE Transactions on Pattern Analysisand Machine Intelligence, 2000, 22(2): 179～190
[44]王建中,姜昱明.基于Snake模型的视频对象分割和跟踪算法.计算机工程与设计, 2005, 26(7): 1928～1930
[45]姜玲玉. NURBS曲线曲率连续延拓的一种有效算法.计算机工程与设计, 2004, 25(12): 2340～2342
[46] Gene C H Chuang, C C Jay Kuo. Wavelet descriptor of planar curves theory and applications. IEEE Trans. Image Processing, 1996, 5(1): 56～70
[47] Y P Wang, S L Lee. Multiscale curvature-based shape representation using B-spline wavelets. IEEE Trans. Image Processing, 1999, 8(11): 1586～1592
[48]王博,潘泉,张洪才等.图像平滑与边缘检测的模糊向量描述.小型微型计算机系统, 1999, 20(3): 218～221
[49] Canny John. A Computational Approach to Edge Detection. IEEE Transactions on PatternAnalysis and Machine Intelligence, 1986, PAMI-8(6): 679～698