车牌自动识别系统的设计与字符识别算法研究
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摘要
随着经济的发展,道路交通压力已经成为亟待解决的问题之一,对交通控制、安全管理也提出了更高的要求,这时,运用电子信息技术的车牌识别系统(License Plate Recognition)便应运而生,车牌识别系统的设计,是现代化的智能交通系统得以实现的重要保证。它可以很好地运用在高速公路自动收费系统、大型安全停车场、高速公路车流量统计等场合中。
当前,车牌识别很多都是基于PC平台来完成的,当然其具有很容易实现的优势,但是成本高、实时性不强,在稳定性方面表现也比较差,为了更好地实现车牌系统的功能,实现脱机工作,本文所采用的硬件系统为单DSP+FPGA和双DSP+FPGA双板子的方式来共同实现。当然,为了更好地解决系统的识别率和成本问题,车牌识别算法也就成为了系统设计的核心问题之一,硬件系统中,本人完成的主要工作是:
(1)对整个硬件系统模块进行熟悉,并完成系统液晶显示模块的调试工作,以使得系统实现可视化;基于CSL对DSP上的底层驱动进行编写。
(2)完成车牌识别算法往硬件嵌入式系统中的移植并对其优化,同时完成实验场景下的系统性能测试。
软件设计中,在经过系统前端的视频采集装置获取图像后,需要完成对车牌的预处理,之后要很好地定位出车牌区域,接着对其进行车牌字符的分割,以获得后期车牌字符识别的可靠的数据源,在本文中,车牌字符识别算法是关键,为了很好地完成识别算法,我们采用的是将彩色图像转化为二值图像,同时,运用支持向量机(Support Vector Machine, SVM)对分割出来的字符图像进行多分类识别。在对多种核函数在车牌识别中的性能、识别率等综合考虑后,我们提出用径向基核来进行车牌字符识别,训练之前,需要对字符进行必要的预处理,以更好地提高识别率。
本文在继承团队成员对车牌字符进行定位和分割之后,完成车牌字符识别算法设计,算法已能够在软件平台上正确实现,识别率良好。
With the economic development, modern traffic pressure has become one of the problems which should be given top priority to solve,and also, traffic control, safety management has been put forward higher requirements,At this point, license plate recognition system (License Plate Recognition) which based on electronic information technology has come into being,License Plate Recognition System is an important guarantee for modern intelligent transportation systems to realize. It can be well used in many occasions, such as: highway automation electronic toll collection system, large security parking place, highway traffic volume statistics, and so on.
At present, when it comes to license plate recognition, most of them are based on PC platform to work, and obviously, it’s very easy to realize, but as for characteristic of cost, real-time, it’s not feasible, and also, the stability of performance is relatively poor, in order to achieve the license plate system function better, and make it can work offline. In this paper, a single DSP + FPGA and dual-DSP + FPGA dual-board approach are adopted. Of course, in order to solve the system's recognition rates and costs better, license plate recognition system design algorithm has become one of the core issues. In hardware systems, complete the main tasks which I am responsible for are:
(1) To get familiar with the hardware system, and finish the debugging work on the liquid display part; Programming driving section base on CSL for DSP.
(2) To transplant license plate recognition algorithm to the embedded systems and to optimize the program, and then, to complete real environment test for system to check its function good or not.
In software design part, after obtaining images from system front-end video capture device, something we should do is to complete the pretreatment of the plate, then to properly locate the plate region, and then we should finish license plate character segmentation, in order to obtain reliable data sources for character recognition. In this article, License Plate Recognition is the key. In order to complete the identification algorithm well, we have adopted the way to transform color images into binary images, and to split up the characters in the image on the multi-classification by the use of support vector machine (Support Vector Machine, SVM). By comprehensive consideration and comparison of kernel functions in the vehicle license plate recognition performance, recognition rate, we propose to use RBF kernel to help us complete the license plate character recognition. Before the training, some preprocessing is essential, so as to increase recognition rate better.
In this paper, after understanding the work of team members about license location and license plate character segmentation, I finished the design of license plate character recognition algorithm, the algorithm has been able to achieve on software platform correctly, and turned out a good recognition rate.
当前,车牌识别很多都是基于PC平台来完成的,当然其具有很容易实现的优势,但是成本高、实时性不强,在稳定性方面表现也比较差,为了更好地实现车牌系统的功能,实现脱机工作,本文所采用的硬件系统为单DSP+FPGA和双DSP+FPGA双板子的方式来共同实现。当然,为了更好地解决系统的识别率和成本问题,车牌识别算法也就成为了系统设计的核心问题之一,硬件系统中,本人完成的主要工作是:
(1)对整个硬件系统模块进行熟悉,并完成系统液晶显示模块的调试工作,以使得系统实现可视化;基于CSL对DSP上的底层驱动进行编写。
(2)完成车牌识别算法往硬件嵌入式系统中的移植并对其优化,同时完成实验场景下的系统性能测试。
软件设计中,在经过系统前端的视频采集装置获取图像后,需要完成对车牌的预处理,之后要很好地定位出车牌区域,接着对其进行车牌字符的分割,以获得后期车牌字符识别的可靠的数据源,在本文中,车牌字符识别算法是关键,为了很好地完成识别算法,我们采用的是将彩色图像转化为二值图像,同时,运用支持向量机(Support Vector Machine, SVM)对分割出来的字符图像进行多分类识别。在对多种核函数在车牌识别中的性能、识别率等综合考虑后,我们提出用径向基核来进行车牌字符识别,训练之前,需要对字符进行必要的预处理,以更好地提高识别率。
本文在继承团队成员对车牌字符进行定位和分割之后,完成车牌字符识别算法设计,算法已能够在软件平台上正确实现,识别率良好。
With the economic development, modern traffic pressure has become one of the problems which should be given top priority to solve,and also, traffic control, safety management has been put forward higher requirements,At this point, license plate recognition system (License Plate Recognition) which based on electronic information technology has come into being,License Plate Recognition System is an important guarantee for modern intelligent transportation systems to realize. It can be well used in many occasions, such as: highway automation electronic toll collection system, large security parking place, highway traffic volume statistics, and so on.
At present, when it comes to license plate recognition, most of them are based on PC platform to work, and obviously, it’s very easy to realize, but as for characteristic of cost, real-time, it’s not feasible, and also, the stability of performance is relatively poor, in order to achieve the license plate system function better, and make it can work offline. In this paper, a single DSP + FPGA and dual-DSP + FPGA dual-board approach are adopted. Of course, in order to solve the system's recognition rates and costs better, license plate recognition system design algorithm has become one of the core issues. In hardware systems, complete the main tasks which I am responsible for are:
(1) To get familiar with the hardware system, and finish the debugging work on the liquid display part; Programming driving section base on CSL for DSP.
(2) To transplant license plate recognition algorithm to the embedded systems and to optimize the program, and then, to complete real environment test for system to check its function good or not.
In software design part, after obtaining images from system front-end video capture device, something we should do is to complete the pretreatment of the plate, then to properly locate the plate region, and then we should finish license plate character segmentation, in order to obtain reliable data sources for character recognition. In this article, License Plate Recognition is the key. In order to complete the identification algorithm well, we have adopted the way to transform color images into binary images, and to split up the characters in the image on the multi-classification by the use of support vector machine (Support Vector Machine, SVM). By comprehensive consideration and comparison of kernel functions in the vehicle license plate recognition performance, recognition rate, we propose to use RBF kernel to help us complete the license plate character recognition. Before the training, some preprocessing is essential, so as to increase recognition rate better.
In this paper, after understanding the work of team members about license location and license plate character segmentation, I finished the design of license plate character recognition algorithm, the algorithm has been able to achieve on software platform correctly, and turned out a good recognition rate.
引文
[1]尹仕健,段国钦.高速公路车牌自动识别系统应用探讨.广东公路交通,2003(4)
[2]中华人民共和国公安部.中华人民共和国机动车号牌. GA36-2007行业标准
[3]岳长洪.浅谈基于DSP硬件识别的车牌识别系统.北方交通,2008年第6期:187-189
[4]甘雷.车辆拍照识别系统(LPRS)研究.北方工业大学,硕士学位论文,2002年5月:1-6.
[5] E. R. LEE, P. K. KIM. Automatic recognition of a car license plate using color image processing [A]. Proc of International Conference on Image Processing(ICIP94)[C], 1994, 12:301-305
[6] Jia Wenjing, Zhang Huaifeng, He Xiangjian. Region-based license plate detection. Journal of Network and Computer Applications, 30, 4, 1324-1333, November 2007
[7] Jia Li, Mei Xie. A Color and Texture Feature Based Approach to License Plate Location. 2007 International Conference on Computational Intelligence and Security (CIS-2007) Harbin, China, 15-19 Dec. 2007, 376-380
[8] Sirithinaphong, T. Chamnongthai. K. Extraction of car license plate using motor vehicle regulation and character pattern recognition. Circuits and Systems, 1998, IEEE APCCAS
[9] Fu K.S., Mui J.K.. A survey on image segmentation. Pattern Recognition, 1981, Vol.13, No.3, 3-16
[10] R. Bremananth, Dr. A. Chitra. A Robust Video Based License Plate Recognition System. ICISIP, 2005, 175.
[11] Cheng K.S. et al. The application of competitive Hopfield neural network to medical image segmentation. IEEE-MI, 1996, Vol.15, 560-567
[12]刘志鹏,朱清新.基于实时视频流的车牌定位与识别算法的研究.中国优秀硕士学位论文全文数据库. 2008
[13] Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines[J] , IEEE Transactions on Neural Networks, 2002, 13(1): 415-425
[14] Steve R. Gunn,Support Vector Machines for Classification and Regression,Technical Report, 10 May 1998
[15] Keerthi S S, Lin C J. Asymptotic behaviors of support vector machines with Gaussian kernel [J]. Neural Computation. 2003, 15(7): 1667-1689
[16] Ashok N. Srivastava,Santanu Das,Classification of Damage Signatures in Composite Platesusing One-Class SVMs,February 18, 2009
[17]陈是知.μC/OS-Ⅱ内核分析、移植与驱动程序开发.北京:人民邮电出版社,2007.9
[18]覃征.程序设计方法与优化.西安交通大学出版社,2004
[19]艾孟奇.H.264标准视频解码优化及DSP程序设计,硕士学位论文,电子科技大学,2007
[20]德州仪器公司技术文档. Code Composer Studio开发环境用户文档,1999
[21]谭浩强. C程序设计教程.北京:清华大学出版社,2002
[22]李波,曾致远,周建中,罗勤.车牌识别系统研究与实现.计算机技术与发展,2006年第6期:10-11,14
[23]德州仪器公司技术文档. TMS320C6713B浮点数字信号处理器,2004
[24]德州仪器公司技术文档. TMS320C6000 Chip Support Library技术参考文档,2004
[25]边肇祺,张学工,等.模式识别[M].第二版.北京:清华大学出版社, 2006
[26]崔江,王友仁.车牌自动识别方法中的关键技术研究.计算机测量与控制,2003.11(4)260-262
[27]德州仪器公司技术文档. TMS320C6000 GPIO通用输入输出接口参考指南,2004
[28]德州仪器公司技术文档.TMS320C6000 EMIF外接SDRAM/SGRAM接口参考指南,1999
[29]韩智广.车牌分割与矫正.计算机工程与应,2003(9):210-212
[30]赵雪春,戚飞虎.基于彩色分割的车牌自动识别技术.上海交通大学学报,1998.32(10):4-9
[31]彭启琮,张诗雅,常冉等编译. TI DSP集成化开发环境(CCS)使用手册.北京:清华大学出版社,2005
[32]彭启琮,管庆. DSP集成开发环境——CCS及DPS/BIOS的原理与应用.北京:电子工业出版社, 2004
[33]刘兴,蒋天发,车牌字符图像分割技术的研究与应用.湖北,武汉,武汉大学学报
[34]李方慧,王飞,何佩琨. TMS320C6000系列DSPs原理与应用(第二版).北京:电子工业出版社,2003
[35] Qing Li著,王安生译.嵌入式系统的实时概念.北京:北京航空航天大学出版社,2004
[36] [美]Jean J.Labrosse/著;邵贝贝等译.嵌入式实时操作系统UC/OS-II.北京:北京航空航天大学出版社,2003
[2]中华人民共和国公安部.中华人民共和国机动车号牌. GA36-2007行业标准
[3]岳长洪.浅谈基于DSP硬件识别的车牌识别系统.北方交通,2008年第6期:187-189
[4]甘雷.车辆拍照识别系统(LPRS)研究.北方工业大学,硕士学位论文,2002年5月:1-6.
[5] E. R. LEE, P. K. KIM. Automatic recognition of a car license plate using color image processing [A]. Proc of International Conference on Image Processing(ICIP94)[C], 1994, 12:301-305
[6] Jia Wenjing, Zhang Huaifeng, He Xiangjian. Region-based license plate detection. Journal of Network and Computer Applications, 30, 4, 1324-1333, November 2007
[7] Jia Li, Mei Xie. A Color and Texture Feature Based Approach to License Plate Location. 2007 International Conference on Computational Intelligence and Security (CIS-2007) Harbin, China, 15-19 Dec. 2007, 376-380
[8] Sirithinaphong, T. Chamnongthai. K. Extraction of car license plate using motor vehicle regulation and character pattern recognition. Circuits and Systems, 1998, IEEE APCCAS
[9] Fu K.S., Mui J.K.. A survey on image segmentation. Pattern Recognition, 1981, Vol.13, No.3, 3-16
[10] R. Bremananth, Dr. A. Chitra. A Robust Video Based License Plate Recognition System. ICISIP, 2005, 175.
[11] Cheng K.S. et al. The application of competitive Hopfield neural network to medical image segmentation. IEEE-MI, 1996, Vol.15, 560-567
[12]刘志鹏,朱清新.基于实时视频流的车牌定位与识别算法的研究.中国优秀硕士学位论文全文数据库. 2008
[13] Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines[J] , IEEE Transactions on Neural Networks, 2002, 13(1): 415-425
[14] Steve R. Gunn,Support Vector Machines for Classification and Regression,Technical Report, 10 May 1998
[15] Keerthi S S, Lin C J. Asymptotic behaviors of support vector machines with Gaussian kernel [J]. Neural Computation. 2003, 15(7): 1667-1689
[16] Ashok N. Srivastava,Santanu Das,Classification of Damage Signatures in Composite Platesusing One-Class SVMs,February 18, 2009
[17]陈是知.μC/OS-Ⅱ内核分析、移植与驱动程序开发.北京:人民邮电出版社,2007.9
[18]覃征.程序设计方法与优化.西安交通大学出版社,2004
[19]艾孟奇.H.264标准视频解码优化及DSP程序设计,硕士学位论文,电子科技大学,2007
[20]德州仪器公司技术文档. Code Composer Studio开发环境用户文档,1999
[21]谭浩强. C程序设计教程.北京:清华大学出版社,2002
[22]李波,曾致远,周建中,罗勤.车牌识别系统研究与实现.计算机技术与发展,2006年第6期:10-11,14
[23]德州仪器公司技术文档. TMS320C6713B浮点数字信号处理器,2004
[24]德州仪器公司技术文档. TMS320C6000 Chip Support Library技术参考文档,2004
[25]边肇祺,张学工,等.模式识别[M].第二版.北京:清华大学出版社, 2006
[26]崔江,王友仁.车牌自动识别方法中的关键技术研究.计算机测量与控制,2003.11(4)260-262
[27]德州仪器公司技术文档. TMS320C6000 GPIO通用输入输出接口参考指南,2004
[28]德州仪器公司技术文档.TMS320C6000 EMIF外接SDRAM/SGRAM接口参考指南,1999
[29]韩智广.车牌分割与矫正.计算机工程与应,2003(9):210-212
[30]赵雪春,戚飞虎.基于彩色分割的车牌自动识别技术.上海交通大学学报,1998.32(10):4-9
[31]彭启琮,张诗雅,常冉等编译. TI DSP集成化开发环境(CCS)使用手册.北京:清华大学出版社,2005
[32]彭启琮,管庆. DSP集成开发环境——CCS及DPS/BIOS的原理与应用.北京:电子工业出版社, 2004
[33]刘兴,蒋天发,车牌字符图像分割技术的研究与应用.湖北,武汉,武汉大学学报
[34]李方慧,王飞,何佩琨. TMS320C6000系列DSPs原理与应用(第二版).北京:电子工业出版社,2003
[35] Qing Li著,王安生译.嵌入式系统的实时概念.北京:北京航空航天大学出版社,2004
[36] [美]Jean J.Labrosse/著;邵贝贝等译.嵌入式实时操作系统UC/OS-II.北京:北京航空航天大学出版社,2003