汽车牌照识别技术研究
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摘要
随着城市化步伐加快,机动车日益普及,目前许多国家都存在交通事故频发、交通拥堵情况严重等问题。如何高效的进行交通管理,已经成为世界各国关注的焦点。在这种大的背景下,伴随着计算机技术、通信技术、信息技术的飞速发展,智能交通系统(ITS,Intelligence Traffic System)也随之诞生,并且已经成为当前交通管理发展的主要方向。车辆牌照识别(LPR)系统作为智能交通系统的核心,起着非常关键的作用。汽车牌照的自动识别是计算机视觉与模式识别技术在智能交通领域应用的重要研究课题之一,是实现智能化交通的重要部分。本文针对目前车牌识别技术存在的一些问题,研究了车牌识别的各项关键技术,在分析了近年来一些典型的车牌识别算法的基础上,最终确定一系列有效的算法对车牌进行识别。车辆牌照识别系统分为车牌定位、车牌字符分割及字符识别三大部分,它的研究主要涉及到了模式识别、人工智能、计算机视觉、数字图像处理以及人工神经网络等众多的学科领域。在车牌定位方面,车牌定位是指将车牌区域从车辆图像中定位并分割出来,是车牌识别的基础。为了找到有效的车牌定位方法,本文对车牌特征信息进行了归纳总结,介绍了车牌定位前的图像预处理技术,并根据车牌区域在水平、垂直两个方向的纹理信息较其他区域都更加丰富的特征,介绍了车牌区域定位方法。在车牌字符分割方面,分析了车牌图像二值化、倾斜矫正以及字符归一化等各种算法,研究了基于字符块提取的字符分割方法。在字符识别方面,概括比较了常用的字符识别方法,对字符预处理、特征向量的提取进行了详细分析,研究了基于BP神经网络算法的字符识别方法。通过实验,识别率超过90%,证明本文采用的技术是比较成功和可行的。
With the acceleration of urbanization and increasing popularity of motor vehicles, now in many countries there are frequent traffic accidents and jams. How to manage the traffic efficiently has become the focus of attention around the world. In this environment, along with the rapid development of computer technology, communications technology and information technology, the intelligent transportation system (ITS) has become the main development direction of the current traffic management. As the core of ITS, the License Plate Recognition (LPR) System plays a very important role. Focus on some problems of LPR, this paper studies the LPR key technologies. Basing on the analysis of some typical recognition algorithms, this paper finally settles down a series of effective license plate recognition algorithms. The LPR system is composed of license plate location, character segmentation and character recognition. The LPR system involves numerous discipline domains, such as pattern recognition, artificial intelligence, computer vision, digital image processing and artificial neural network etc. The purpose of license plate location is to separate the vehicle license plate from the image. In part of location, this paper first summarizes the characteristic information of license plate, then introduces a series of image pre-processing technology. The method of location is on the basis of more abundant characteristic information on the level and vertical direction of license plate than other area. In character segmentation, this article analyzes a lot of algorithms about license plate image binarization, tilt correction, character uniform. According to the character block, it studies the character segmentation method. In character recognition, this paper summaries and compares common character recognition algorithms. In addition to above, it also analyzes detailedly the method of character pre-processing, feature vector extraction and uses character recognition method on the basis of BP neural network. Through experiments, recognition rate is over 90%. It proves that technology used in this article is successful and feasible.
With the acceleration of urbanization and increasing popularity of motor vehicles, now in many countries there are frequent traffic accidents and jams. How to manage the traffic efficiently has become the focus of attention around the world. In this environment, along with the rapid development of computer technology, communications technology and information technology, the intelligent transportation system (ITS) has become the main development direction of the current traffic management. As the core of ITS, the License Plate Recognition (LPR) System plays a very important role. Focus on some problems of LPR, this paper studies the LPR key technologies. Basing on the analysis of some typical recognition algorithms, this paper finally settles down a series of effective license plate recognition algorithms. The LPR system is composed of license plate location, character segmentation and character recognition. The LPR system involves numerous discipline domains, such as pattern recognition, artificial intelligence, computer vision, digital image processing and artificial neural network etc. The purpose of license plate location is to separate the vehicle license plate from the image. In part of location, this paper first summarizes the characteristic information of license plate, then introduces a series of image pre-processing technology. The method of location is on the basis of more abundant characteristic information on the level and vertical direction of license plate than other area. In character segmentation, this article analyzes a lot of algorithms about license plate image binarization, tilt correction, character uniform. According to the character block, it studies the character segmentation method. In character recognition, this paper summaries and compares common character recognition algorithms. In addition to above, it also analyzes detailedly the method of character pre-processing, feature vector extraction and uses character recognition method on the basis of BP neural network. Through experiments, recognition rate is over 90%. It proves that technology used in this article is successful and feasible.
引文
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[23].王占全,徐慧.精通Visual C++数字图像处理技术与工程案例[M].北京:人民邮电出版社,2009:1-462
[24].姚丽娜,訾世庆,李文刚,等.多媒体技术及应用[M].北京:地质出版社,2007:79-89
[25].沈庭芝,王卫江,闫雪梅.数字图像处理及模式识别[M].北京:北京理工大学出版社,2007:1-263
[26].刘文耀.数字图像采集与处理[M].北京:电子工业出版社,2007:1-336
[27].杨枝灵,王开.Visual C++数字图像获取处理及实践应用[M].北京:人民邮电出版社,2003:1-595
[28].赵启升,李存华.基于VC++的车牌识别系统关键技术研究[J].计算机科学,2006,33(12):207-209,213
[29].王海娇,李文举,王新年,等.基于边缘颜色聚类和神经网络的车牌类型识别[J].计算机工程与应用,2008,44(26):196-199
[30].刘智勇.智能交通控制理论及其应用[M].北京:科学出版社,2003:1-253
[31].尚松浩.水资源系统分析方法及应用[M].北京:清华大学出版社,2006:192-201
[32].吴斌,吴亚东,张红英.基于变分偏微分方程的图像复原技术[M].北京:北京大学出版社,2008:70-79
[33].巨永锋.智能控制和智能自动化[J].西安公路交通大学学报,2001,21(3):111-114.
[34].刘伟铭,赵雪平.一种基于扫描行的汽车车牌定位算法[J].计算机工程与应用,2004, 6:223-225
[35].邵立福,张欲保,毛宁,等.基于BP神经网络的某挖掘机故障诊断知识库设计[J].土石方机械与施工技术,2007,5:52-55.
[36].巨永锋,蔺广逢,蔡占华.基于遗传算法的图像识别方法[J].长安大学学报(自然科学版),2004,24(6):98-101.
[37].赵素蕊,柳冬青.智能车牌识别技术[J].商业科技,2007,504:19-20.
[38].邹晓涛,陈学.汽车牌照自动识别系统的设计与研制[J].红外与激光工程,2002,31(5):415-418.
[39].吴李汉,文俊浩.车牌自动识别系统的设计与实现[J].机器视觉,2006,9:97-103.
[40].蒋治华,陈继荣,刘奕.车牌去噪技术研究[J].计算机工程,2004,30(24):112-115.
[2].李卫平.智能交通技术应用[M].北京:人民交通出版社,2006:1-13
[3].阎建国,高亮,卢京潮.图象处理技术在车牌识别中的应用[J].电子技术应用,2000,1:17-18
[4].陈友仁,赵正校.基于隐马尔可夫模型的车牌自动识别技术[J].红外与激光工程,2001,30(2):102-107
[5].范勇,蒋欣荣,游志胜,等.汽车牌照快速定位算法[J].光电工程,2001,28(2):56-59
[6].万国红,王敏,黄心汉,等.基于神经网络的汽车牌照自动识别技术研究[J].计算机工程与应用,2002,06:204-205,226
[7].吴大勇,魏平,侯朝桢,等.一种车牌图像中的字符快速分割与识别方法[J].计算机工程与应用,2002,03:232-233
[8].杨卫平,李吉成,沈振康.车牌目标的自动定位技术[J].中国图像图形学报,2002,7(8):835-839
[9].王飞,刘镰斧,李在铭.视频目标标识文本视觉特征与模糊识别[J].仪器仪表学报,2002,23(3)增刊:645-646
[10].韩智广,老松杨,谢毓湘,等.车牌分割与矫正[J].计算机工程与应用,2003,09:210-212
[11].冯国进,顾国华,郑瑞红.基于自适应投影方法的快速车牌定位[J].红外与激光工程,2003,32(3):285-287,308
[12].段震,鲁杰,张铃.基于交叉覆盖神经网络的车牌识别研究[J].安徽大学学报,2004,28(5):11-14
[13].罗帆,陈晟,王敏,等.一种基于边缘特征的汽车牌照定位算法[J].华中科技大学学报,2004,32增刊:97-99
[14].高珊,刘万春,朱玉文.基于SVM车牌字符分割和识别方法[J].微电子学与计算机,2005,22(6):34-36
[15].宋焕生,赵祥模,王国强.一种高性能的牌照识别系统[J].计算机工程与应用,2006,17:199-201
[16].秦钟,徐建闽,史胜利,等.基于方向轮廓的小波分解车牌字符识别方法[J].微计算 机信息,2006,22(8-1):229-231
[17].王枚,王国宏.利用伴生与互补颜色特征的车牌定位新方法[J].计算机工程与应用,2007,43(1):206-208,211
[18].张晓波,林勇,刘广起.基于DCT变换的车牌定位算法[J].数学的实践与认知,2007,37(7):64-68
[19].张玲,刘勇,何伟.自适应遗传算法在车牌定位中的应用[J].计算机应用,2008,28(1):184-186
[20].张宏林.Visual C++数字图像模式识别技术及工程实践[M].北京:人民邮电出版社,2003:1-476
[21].Tinku Acharya,Ajoy K.Ray著.田浩,葛秀慧,王顶译.数字图像处理原理与应用[M].北京:清华大学出版社,2007:1-307
[22].张凌雯,顾兆旭.多媒体技术[M].第2版.大连:大连理工大学,2006:150-151
[23].王占全,徐慧.精通Visual C++数字图像处理技术与工程案例[M].北京:人民邮电出版社,2009:1-462
[24].姚丽娜,訾世庆,李文刚,等.多媒体技术及应用[M].北京:地质出版社,2007:79-89
[25].沈庭芝,王卫江,闫雪梅.数字图像处理及模式识别[M].北京:北京理工大学出版社,2007:1-263
[26].刘文耀.数字图像采集与处理[M].北京:电子工业出版社,2007:1-336
[27].杨枝灵,王开.Visual C++数字图像获取处理及实践应用[M].北京:人民邮电出版社,2003:1-595
[28].赵启升,李存华.基于VC++的车牌识别系统关键技术研究[J].计算机科学,2006,33(12):207-209,213
[29].王海娇,李文举,王新年,等.基于边缘颜色聚类和神经网络的车牌类型识别[J].计算机工程与应用,2008,44(26):196-199
[30].刘智勇.智能交通控制理论及其应用[M].北京:科学出版社,2003:1-253
[31].尚松浩.水资源系统分析方法及应用[M].北京:清华大学出版社,2006:192-201
[32].吴斌,吴亚东,张红英.基于变分偏微分方程的图像复原技术[M].北京:北京大学出版社,2008:70-79
[33].巨永锋.智能控制和智能自动化[J].西安公路交通大学学报,2001,21(3):111-114.
[34].刘伟铭,赵雪平.一种基于扫描行的汽车车牌定位算法[J].计算机工程与应用,2004, 6:223-225
[35].邵立福,张欲保,毛宁,等.基于BP神经网络的某挖掘机故障诊断知识库设计[J].土石方机械与施工技术,2007,5:52-55.
[36].巨永锋,蔺广逢,蔡占华.基于遗传算法的图像识别方法[J].长安大学学报(自然科学版),2004,24(6):98-101.
[37].赵素蕊,柳冬青.智能车牌识别技术[J].商业科技,2007,504:19-20.
[38].邹晓涛,陈学.汽车牌照自动识别系统的设计与研制[J].红外与激光工程,2002,31(5):415-418.
[39].吴李汉,文俊浩.车牌自动识别系统的设计与实现[J].机器视觉,2006,9:97-103.
[40].蒋治华,陈继荣,刘奕.车牌去噪技术研究[J].计算机工程,2004,30(24):112-115.