便携式菌落智能计数系统的设计和实现
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摘要
菌落检测是农业、食品、医药卫生等行业进行质量检测的重要方法之一。依靠肉眼观察的人工检测方法速度慢、结果重复性差,而且工作繁重乏味,工作效率低。目前,市场上虽然出现了菌落自动计数仪,但大都以PC机作为运算平台,便携性差,难以满足实时检测要求。近年来嵌入式技术飞速发展,嵌入式设备在运算速度和存储容量上都有了长足的进步,嵌入式操作系统的发展也日趋成熟。由于嵌入式设备本身具有便携性好、成本低的特点,使嵌入式技术得到了更为广泛的应用。
针对菌落检测对实时性和便携性的要求,本研究结合嵌入式技术,设计了基于ARM平台的嵌入式菌落智能计数系统,并完成了原型样机的开发。系统以S3C6410作为核心处理器,采用广角红外CCD摄像头作为视频采集设备。为了提高嵌入式菌落智能计数系统的整体运行效率,我们对系统使用的Windows CE 6.0内核进行了裁剪和定制;为了达到最好的检测效果,本设计对样机结构和照明系统进行了精心设计。
菌落自动计数算法是整个系统性能优劣的关键。我们使用OPENCV计算机视觉库开发了菌落智能计数算法,研究了一种基于霍夫变换的培养皿边缘提取算法,取得了良好的效果。粘连菌落的分割是菌落自动计数算法的难点,本文提出了一种基于迭代腐蚀算法的菌落计数算法,并通过多次条件膨胀的方法消除了传统迭代腐蚀算法对较大菌落的“过分割”问题,使平均计数偏差控制在6%以内,取得了很好的效果。最后,本文完成了算法的移植和PC演示版软件的开发,并最终实现了便携式菌落智能计数系统的开发和测试工作;
总之,本研究对菌落自动计数算法进行了有意义的探索和尝试,并在此基础上开发了嵌入式样机系统,研究成果具有一定的理论意义和应用价值。
Colony detection is an important method of quality inspection in agriculture, food and medicine industries. But it is a hard and boring work to detect colony using the method of traditional visual check. Currently, most of the colony automatic counter is developed based on PC platform which has poor portability and could not meet the requirements of field detection. With the rapid development of embedded technology, the embedded device which has the advantages of low cost and good portability has been used more and more widely.
An embedded intelligent colony counting system based on ARM was designed in this paper and a kind of embedded colony automatic counter prototype was developed. The S3C6410 chip was used as the CPU of the system. The wide-angle infrared camera was chosen for colony image acquisition and a customized Windows CE 6.0 kernel was designed to improve system efficiency. Prototype structure and the lighting system were well designed for the best results.
In this paper, an intelligent automatic colony counting algorithm was developed with the OPENCV computer vision library and was tested both on PC and ARM platform. An algorithm of automatic edge detection was developed to improve the detection accuracy based on Hough Transform. We also proposed a colony automatic counting algorithm based on an improved iterative erosion algorithm to avoid the redundant calculations. The average deviation of counting results was controlled in 6% comparing with gross inspection results.
In a word, some significant research and attempt were proposed in the study of colony counting algorithm. A kind of embedded automatic colony counter prototype was developed and the intelligent colony counting algorithm was successfully transplanted on the target embedded platform. Therefore, all of these works not only have theoretical significance but also have extensive practical value for instrument development application.
针对菌落检测对实时性和便携性的要求,本研究结合嵌入式技术,设计了基于ARM平台的嵌入式菌落智能计数系统,并完成了原型样机的开发。系统以S3C6410作为核心处理器,采用广角红外CCD摄像头作为视频采集设备。为了提高嵌入式菌落智能计数系统的整体运行效率,我们对系统使用的Windows CE 6.0内核进行了裁剪和定制;为了达到最好的检测效果,本设计对样机结构和照明系统进行了精心设计。
菌落自动计数算法是整个系统性能优劣的关键。我们使用OPENCV计算机视觉库开发了菌落智能计数算法,研究了一种基于霍夫变换的培养皿边缘提取算法,取得了良好的效果。粘连菌落的分割是菌落自动计数算法的难点,本文提出了一种基于迭代腐蚀算法的菌落计数算法,并通过多次条件膨胀的方法消除了传统迭代腐蚀算法对较大菌落的“过分割”问题,使平均计数偏差控制在6%以内,取得了很好的效果。最后,本文完成了算法的移植和PC演示版软件的开发,并最终实现了便携式菌落智能计数系统的开发和测试工作;
总之,本研究对菌落自动计数算法进行了有意义的探索和尝试,并在此基础上开发了嵌入式样机系统,研究成果具有一定的理论意义和应用价值。
Colony detection is an important method of quality inspection in agriculture, food and medicine industries. But it is a hard and boring work to detect colony using the method of traditional visual check. Currently, most of the colony automatic counter is developed based on PC platform which has poor portability and could not meet the requirements of field detection. With the rapid development of embedded technology, the embedded device which has the advantages of low cost and good portability has been used more and more widely.
An embedded intelligent colony counting system based on ARM was designed in this paper and a kind of embedded colony automatic counter prototype was developed. The S3C6410 chip was used as the CPU of the system. The wide-angle infrared camera was chosen for colony image acquisition and a customized Windows CE 6.0 kernel was designed to improve system efficiency. Prototype structure and the lighting system were well designed for the best results.
In this paper, an intelligent automatic colony counting algorithm was developed with the OPENCV computer vision library and was tested both on PC and ARM platform. An algorithm of automatic edge detection was developed to improve the detection accuracy based on Hough Transform. We also proposed a colony automatic counting algorithm based on an improved iterative erosion algorithm to avoid the redundant calculations. The average deviation of counting results was controlled in 6% comparing with gross inspection results.
In a word, some significant research and attempt were proposed in the study of colony counting algorithm. A kind of embedded automatic colony counter prototype was developed and the intelligent colony counting algorithm was successfully transplanted on the target embedded platform. Therefore, all of these works not only have theoretical significance but also have extensive practical value for instrument development application.
引文
[1]罗旺,基于遗传算法的菌落图像分割:[硕士学位论文],成都,电子科技大学,2007
[2]门洪,武玉杰,王彩红等.基于图像处理的异养菌菌落计数方法研究,检测与仪表,2008,35(3):38~41
[3]刘相滨,类圆性颗粒分割技术研究:[博士学位论文],湖南,湖南大学,2006
[4]刘莉莉,类圆性颗粒分割技术研究:[硕士毕业论文],重庆,重庆大学,2009
[5] G.Corkidi, R Diaz-uribe, COVASIAM:an image analysis method that allows detection of confluent microbial colonies and colonies of various sizes for automated counting, Applied and Environmental Microbiology, 1998,4:1400-1404.
[6] Giuseppe Gallas, Automation in chromosome classification and cell analysis, Pattern Recognition of Biomedical Objects, 1978, 10:4-9.
[7]Liang Ji, Intelligent Splitting In the Chromosome Domain, Pattern Recognition.1989, 22(5):519-532.
[8]Gady Agam, Its’hak Dinstein. Geometric separation of touching objects applied to automatic chromosome classification, Proc of Int Conf on SPIE. Boston, 1993, 206:277-288.
[9]Jean-Philippe Thiran, Marc-Olivier Becks, etal, Automatic recognition of cancerous cells using mathematical morphology, Proc of Int Conf on SPIE. Rochester, 1994,2359:392-401.
[10]Dwi Anoraganingrum, S Kroner, B Gotfried, Cell segmentation with adaptive region growing, Proc of 10th IEEE Int Conf on Image Analysis and Processing(ICIAP).Venice:1999,09.
[11]Dwi Anoragaingrum.Cell segmentation with median filter and mathematical morphology operation, Proc of 10th IEEE Int Conf on Image Analysis and Processing(ICIAP).Venice, 1999,1043-1046.
[12]Haralick R M, Sternberg S R, Zhuang X H, Image analysis using mathematical morphology, IEEE Trans PAMI,1987,9(4):532-550.
[13]Kim KyungSu, Song Jeongjun, Automatic classification of cells using morphological shape in peripheral blood images, Proc of Int Conf on SPIE Boston, 2000, 4210:290-298.
[14]Casasent David, Talukder Ashit, Cox Westley, etal, Detection and segmentation of multiple touching product inspection items, In:Proc of Int Conf on SPIE.Boston,1996,2907:205-215.
[15]Talukder Ashit, David Casasent, Automated segmentation and feature extraction of product inspection items, In:Proc of Int Conf on SPIE. Boston, 1996, 2907: 96~107.
[16]Casasent David P, Cox Westley, RI-MINACE filters to augment segmentation of touching objects, In:Proc of Int Conf on SPIE. Orlando, 1997, 3037:354~363.
[17]Casasent David p, Talukder Ashit. Classification of agricultural products from X-ray images, In:Proc of Int Conf on SPIE.Boston,1998,3543:53~64.
[18]Talukder Ashit, David Casasent, Ha-Woon Lee.Modified binary watershed transform for segmentation of agricultural products[J], In:Proc of Int Conf on SPIE.Boston,1999,3543:53~64.
[19]Talukder Ashit,David Casasent,Ha-Woon Lee,et al.New feature extraction method for classification of agricultural products from x-ray images, In:Proc of Int Conf on SPIE.Boston,1999,3543:119~130.
[20]Leen-Kiat Soh, Costas Tsatsoulis. Separating touching objects in remote sensing imagery:the restricted growing concept and implementations, In:Proc of IEEE trans. On Image Processing.2000, 19(2):1~8.
[21]凌云,王一鸣,孙明等.基于机器视觉的大米外观品质检测装置,农业机械学报,2005,36(9):89~92
[22]孙明,王一鸣,凌云等,基于色调的黄粒米检测方法[J].农业机械学报,2005,36(8):78~81
[23]候彩云,王一鸣,凌云等,垩白米粒的计算机图像识别[J].农业工程学报,2002,18(3):165~168
[24]周莹莉,曾立波,刘均堂等,基于图像处理的菌落自动计数方法及其实现,数据采集与处理,2003,18(4):460~464
[25]尤育赛,于慧敏.一种重叠红细胞图像的分离方法,中国图像图形学报,2005,10(6):736~740
[26]尤育赛,于慧敏,刘圆圆,.基于粒度测量的重叠圆形颗粒图像分离方法[J].浙江大学学报(工学版),2005,39(7):962~966
[27]宋强,徐科,徐金梧等.基于图象处理的棒材自动计数技,钢铁,2004,39(5):34~37
[28]Tiny6410用户手册,友善之臂公司,2010
[29]中华人民共和国卫生部,GB 4789.2—2010,食品安全国家标准,食品微生物学检验:菌落总数测定
[30]何宗键,Windows CE嵌入式系统,北京:北京航空航天大学出版社,2006.3~10
[31]李忠民,杨刚,顾亦然等,ARM嵌入式VxWorks实践教程,北京:北京航空航天出版社,2006.15~25
[32]徐惠民,基于VxWorks的嵌入式系统及实验,北京:北京邮电大学出版社,2006.20~23
[33]何宗键译,Windows CE 6.0开发者参考,北京:机械工业出版社,2009.35~45
[34]何永琪,嵌入式Linux系统实用开发,北京:电子工业出版社,2010.2~13
[35]Samuel Phung,Windows CE 6.0高级编程,北京:清华大学出版社,2009.10~15.
[39]Canny J,1986,A computational approach to edge detection,IEEE-PAMI,8:679~698.
[40]刘安安,层次化的菌落自动检测方法,黑龙江环境通报,2010,34(4):58~61
[41]三星公司, S3C6410用户参考指南,2009
[42]尤滔,张平,周晓权,Windows CE高级开发指南,北京:电子工业出版社,2000.24~45
[2]门洪,武玉杰,王彩红等.基于图像处理的异养菌菌落计数方法研究,检测与仪表,2008,35(3):38~41
[3]刘相滨,类圆性颗粒分割技术研究:[博士学位论文],湖南,湖南大学,2006
[4]刘莉莉,类圆性颗粒分割技术研究:[硕士毕业论文],重庆,重庆大学,2009
[5] G.Corkidi, R Diaz-uribe, COVASIAM:an image analysis method that allows detection of confluent microbial colonies and colonies of various sizes for automated counting, Applied and Environmental Microbiology, 1998,4:1400-1404.
[6] Giuseppe Gallas, Automation in chromosome classification and cell analysis, Pattern Recognition of Biomedical Objects, 1978, 10:4-9.
[7]Liang Ji, Intelligent Splitting In the Chromosome Domain, Pattern Recognition.1989, 22(5):519-532.
[8]Gady Agam, Its’hak Dinstein. Geometric separation of touching objects applied to automatic chromosome classification, Proc of Int Conf on SPIE. Boston, 1993, 206:277-288.
[9]Jean-Philippe Thiran, Marc-Olivier Becks, etal, Automatic recognition of cancerous cells using mathematical morphology, Proc of Int Conf on SPIE. Rochester, 1994,2359:392-401.
[10]Dwi Anoraganingrum, S Kroner, B Gotfried, Cell segmentation with adaptive region growing, Proc of 10th IEEE Int Conf on Image Analysis and Processing(ICIAP).Venice:1999,09.
[11]Dwi Anoragaingrum.Cell segmentation with median filter and mathematical morphology operation, Proc of 10th IEEE Int Conf on Image Analysis and Processing(ICIAP).Venice, 1999,1043-1046.
[12]Haralick R M, Sternberg S R, Zhuang X H, Image analysis using mathematical morphology, IEEE Trans PAMI,1987,9(4):532-550.
[13]Kim KyungSu, Song Jeongjun, Automatic classification of cells using morphological shape in peripheral blood images, Proc of Int Conf on SPIE Boston, 2000, 4210:290-298.
[14]Casasent David, Talukder Ashit, Cox Westley, etal, Detection and segmentation of multiple touching product inspection items, In:Proc of Int Conf on SPIE.Boston,1996,2907:205-215.
[15]Talukder Ashit, David Casasent, Automated segmentation and feature extraction of product inspection items, In:Proc of Int Conf on SPIE. Boston, 1996, 2907: 96~107.
[16]Casasent David P, Cox Westley, RI-MINACE filters to augment segmentation of touching objects, In:Proc of Int Conf on SPIE. Orlando, 1997, 3037:354~363.
[17]Casasent David p, Talukder Ashit. Classification of agricultural products from X-ray images, In:Proc of Int Conf on SPIE.Boston,1998,3543:53~64.
[18]Talukder Ashit, David Casasent, Ha-Woon Lee.Modified binary watershed transform for segmentation of agricultural products[J], In:Proc of Int Conf on SPIE.Boston,1999,3543:53~64.
[19]Talukder Ashit,David Casasent,Ha-Woon Lee,et al.New feature extraction method for classification of agricultural products from x-ray images, In:Proc of Int Conf on SPIE.Boston,1999,3543:119~130.
[20]Leen-Kiat Soh, Costas Tsatsoulis. Separating touching objects in remote sensing imagery:the restricted growing concept and implementations, In:Proc of IEEE trans. On Image Processing.2000, 19(2):1~8.
[21]凌云,王一鸣,孙明等.基于机器视觉的大米外观品质检测装置,农业机械学报,2005,36(9):89~92
[22]孙明,王一鸣,凌云等,基于色调的黄粒米检测方法[J].农业机械学报,2005,36(8):78~81
[23]候彩云,王一鸣,凌云等,垩白米粒的计算机图像识别[J].农业工程学报,2002,18(3):165~168
[24]周莹莉,曾立波,刘均堂等,基于图像处理的菌落自动计数方法及其实现,数据采集与处理,2003,18(4):460~464
[25]尤育赛,于慧敏.一种重叠红细胞图像的分离方法,中国图像图形学报,2005,10(6):736~740
[26]尤育赛,于慧敏,刘圆圆,.基于粒度测量的重叠圆形颗粒图像分离方法[J].浙江大学学报(工学版),2005,39(7):962~966
[27]宋强,徐科,徐金梧等.基于图象处理的棒材自动计数技,钢铁,2004,39(5):34~37
[28]Tiny6410用户手册,友善之臂公司,2010
[29]中华人民共和国卫生部,GB 4789.2—2010,食品安全国家标准,食品微生物学检验:菌落总数测定
[30]何宗键,Windows CE嵌入式系统,北京:北京航空航天大学出版社,2006.3~10
[31]李忠民,杨刚,顾亦然等,ARM嵌入式VxWorks实践教程,北京:北京航空航天出版社,2006.15~25
[32]徐惠民,基于VxWorks的嵌入式系统及实验,北京:北京邮电大学出版社,2006.20~23
[33]何宗键译,Windows CE 6.0开发者参考,北京:机械工业出版社,2009.35~45
[34]何永琪,嵌入式Linux系统实用开发,北京:电子工业出版社,2010.2~13
[35]Samuel Phung,Windows CE 6.0高级编程,北京:清华大学出版社,2009.10~15.
[39]Canny J,1986,A computational approach to edge detection,IEEE-PAMI,8:679~698.
[40]刘安安,层次化的菌落自动检测方法,黑龙江环境通报,2010,34(4):58~61
[41]三星公司, S3C6410用户参考指南,2009
[42]尤滔,张平,周晓权,Windows CE高级开发指南,北京:电子工业出版社,2000.24~45