基于ARM煤炭灰分在线检测系统
摘要
本文是针对某洗煤厂生产线提出的煤炭灰分在线检测系统。传统检测方法是取一定量待测煤样本，将煤燃烧后检测煤炭灰分(根据燃烧前、后的重量比得出杂质百分含量)，进而判断所生产煤炭灰分是否合格，从而控制洗煤生产设备。此法效率低且无法达到实时控制洗煤系统。
本文针对传统方法的缺点，提出了一种新的基于ARM图像处理方法----利用图像处理方法实现煤炭灰分在线检测。该方法首先通过摄像头摄取煤炭原始灰度图像，然后将原始图像通过线性加权均值滤波器降低噪声，再通过线性灰度变换去除光照影响，最后从概率统计角度以概率及方差为特征，对预处理后的灰度值矩阵提取特征，通过分组特征匹配算法得到所洗煤炭是否合格的判定值，根据判定结果输出一组控制信号达到对洗煤机器的实时控制。
This paper puts forward an Online Assessing System of coal ash targeting the Coal-Washing factory. The tradition approach for this purpose is entirely manual: firstly, we fetch some coal for assessing, and then judge whether the product is consistence with the expectant result by burning the coal to ashes; finally, we adjust the washing system according to the judge. This method not only has a lower efficiency, but also can hardly achieve the real-time control.
To solve the problem, we develop a new image processing approach based on ARM system to implement the online assessing system. This method first obtains the primitive grayscale image of coal from the camera through the linux driver layer. And then reduce the image-noise through a liner-weighting-average filter and eliminate the illumination influence through liner transformation. Finally abstract the main features of the grayscale image matrix based on probability and deviation. By using the group feature matching arithmetic, we can get the final criterion according which to output a set of signals to achieve the real time control on the washing machine.
本文针对传统方法的缺点，提出了一种新的基于ARM图像处理方法----利用图像处理方法实现煤炭灰分在线检测。该方法首先通过摄像头摄取煤炭原始灰度图像，然后将原始图像通过线性加权均值滤波器降低噪声，再通过线性灰度变换去除光照影响，最后从概率统计角度以概率及方差为特征，对预处理后的灰度值矩阵提取特征，通过分组特征匹配算法得到所洗煤炭是否合格的判定值，根据判定结果输出一组控制信号达到对洗煤机器的实时控制。
This paper puts forward an Online Assessing System of coal ash targeting the Coal-Washing factory. The tradition approach for this purpose is entirely manual: firstly, we fetch some coal for assessing, and then judge whether the product is consistence with the expectant result by burning the coal to ashes; finally, we adjust the washing system according to the judge. This method not only has a lower efficiency, but also can hardly achieve the real-time control.
To solve the problem, we develop a new image processing approach based on ARM system to implement the online assessing system. This method first obtains the primitive grayscale image of coal from the camera through the linux driver layer. And then reduce the image-noise through a liner-weighting-average filter and eliminate the illumination influence through liner transformation. Finally abstract the main features of the grayscale image matrix based on probability and deviation. By using the group feature matching arithmetic, we can get the final criterion according which to output a set of signals to achieve the real time control on the washing machine.
引文
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[3] 郑南宁．计算机视觉与模式识别．国防工业出版社，1998
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[8] Don Anderson著．USB系统体系．精英科技译．中国电力出版社，2001
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[21] OmniVision Technologies. OV511+ Advanced Camera to USB Bridge. OmniVision Technologies, Inc. 2000.5
[22] OmniVision Technologies. OV7620 SINGLE-CHIP CMOS VGA COLOR DIGITAL CAMERA. OmniVision Technologies, Inc. 2000.5
[23] Davicom Semiconductor. DM9000 32/16/8-bit Three-In-One Fast Ethernet Controller Thechnical Reference Manual. Davicom Semiconductor, Inc. 1999-2006
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[25] 马忠梅等编著．ARM & Linux嵌入式系统教程．北京：北京航空航天大学出版社，2004
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[30] Robert Love. Linux Kernel Development. Second Edition. China Machine Press, 2006.3
[31] A. Rubini and J. Corbet. Linux Device Driver. Third Edition. O'Reilly and Associates, 2005. 2
[32] 黄凤岗、宋克欧著．模式识别．哈尔滨工程大学出版社，1998．3
[2] 周新论，柳健．数字图像处理．国防工业出版社，1986
[3] 郑南宁．计算机视觉与模式识别．国防工业出版社，1998
[4] 赵炯著．Linux内核完全剖析．机械工业出版社，2006．1
[5] 陈莉君编著．深入分析Linux内核源代码．北京：人民邮电出版社，2002
[6] 张晓林等著．嵌入式系统设计与实践．北京：北京航空航天大学出版社， 2006．1
[7] 慕春棣主编．嵌入式系统的构建．北京：清华大学出版社，2004
[8] Don Anderson著．USB系统体系．精英科技译．中国电力出版社，2001
[9] Jan Axelson著．USB大全．陈逸等译．中国电力出版社，2001
[10] Bill Dirks.Video for Linux Two API Specification.GNU Free Documentation License. 1999-2006
[11] 许海燕，付炎主编．嵌入式系统技术与应用．北京：机械工业出版社，2002
[12] 慕春棣主编．嵌入式系统的构建．北京：清华大学出版社，2004
[13] 许海燕，付炎主编．嵌入式系统技术与应用．北京：机械工业出版社，2002
[14] 马忠梅等编著．ARM嵌入式处理器结构与应用基础．北京：北京航空航天大学出版社，2002
[15] ARM Limited. ARM920T Technical Reference Manual. ARM DDI 0151B,2000.9
[16] 张念淮，江浩编著．USB总线接口开发指南．国防工业出版社，2001
[17] Samsung Electronics. S3C2410A-200MHz & 266MHz 32-Bit RISC Microprocessor User's Manual. Republic of Korea, 2004.3
[18] Samsung Electronics. S3C2410 Reference Board. Republic of Korea, 2002.7
[19] Craig Hollabaugh(美)著．嵌入式Linux：硬件、软件与接口．陈雷，钟书毅等译．北京：电子工业出版社，2003
[20] 周立功等编著．ARM嵌入式系统基础教程．北京：北京航空航天大学出 社，2005
[21] OmniVision Technologies. OV511+ Advanced Camera to USB Bridge. OmniVision Technologies, Inc. 2000.5
[22] OmniVision Technologies. OV7620 SINGLE-CHIP CMOS VGA COLOR DIGITAL CAMERA. OmniVision Technologies, Inc. 2000.5
[23] Davicom Semiconductor. DM9000 32/16/8-bit Three-In-One Fast Ethernet Controller Thechnical Reference Manual. Davicom Semiconductor, Inc. 1999-2006
[24] Frank Vahid，Tony Givargis(美)著．嵌入式系统设计：a unified hardware／software introduction．骆丽译．北京：北京航空航天大学出版社，2004
[25] 马忠梅等编著．ARM & Linux嵌入式系统教程．北京：北京航空航天大学出版社，2004
[26] John Catsoulis(美)著．嵌入式硬件设计．徐君明，许铁军等译．北京：中国电力出版社，2004
[27] 毛德操等编著．Linux内核源代码情景分析。浙江大学出版社，2001．5
[28] 毛德操编著．嵌入式系统：采用公开源代码和StrongARM／Xscale处理器．浙江大学出版社，2003．10
[29] Daniel P. Bovet.Marco Cesati. Understanding the Linux Kernel. Third Editon. O'Reilly, 2005. 11
[30] Robert Love. Linux Kernel Development. Second Edition. China Machine Press, 2006.3
[31] A. Rubini and J. Corbet. Linux Device Driver. Third Edition. O'Reilly and Associates, 2005. 2
[32] 黄凤岗、宋克欧著．模式识别．哈尔滨工程大学出版社，1998．3