实时车牌识别研究及其在智能交通系统中的应用
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摘要
智能运输系统是21世纪现代化交通运输体系的重要发展方向,它是一种信息化、智能化、社会化的新型现代交通系统。随着社会经济的不断发展和人们生活水平的日益提高,整个社会对交通运输的需求不断增大,智能交通系统的应用势在必行。实时车牌识别作为智能交通系统中的一个分支,在大型停车场的管理系统、公共安全、交通管理及有关部门有着特别重要的实际运用价值,正日益受到人们的重视。
本文运用图像处理技术、模式识别技术、车牌定位技术、字符分割技术、神经网络识别技术等来解决车辆牌照识别问题;分为图像预处理、车牌区域定位与几何校正、字符分割与归一化及字符识别四个模块对实时车牌识别进行分析研究。
论文首先研究图像的预处理,将采集到的含有车牌的图像进行灰度化、灰度拉伸、直方图均衡化、中值滤波和图像二值化处理,并改进了一种二值化效果较好的基于灰度直方图的全局最佳平均阈值法;然后对我国车牌特征和常见的车牌定位技术进行深入研究,改进了一种基于车牌区域灰度分布与几何特征的实时车牌定位方法;通过对车牌发生几何形变的原因及类型的分析研究,采用了基于斜率的车牌倾斜校正方法;基于常见的字符分割方法和车牌本身的结构特点及先验知识的分析研究,改进一种基于投影法与车牌先验知识相结合的分割方法;采用邻近插值法,虽然精确度相对较低但可以满足系统的要求且实现方便,将字符归一化为32×16的点阵,为字符的识别做好准备。最后对粒子群优化算法和神经网络的相关原理作了简要介绍和分析研究,在此基础上提出了PSO-BP神经网络并将其用于实时车牌字符识别。
Intelligent Transport System in the 21st century modernization of the transportation system an important development direction, it is a kind of information, intelligence, and a new type of modern society of the transport system. With the continued socio-economic development and increasing the living standards of the people, the entire community of transport demand growing, Intelligent Transportation System Application inevitable. Real-Time License Plate Recognition as a branch of Intelligent Transportation System ,in the large car park management system, public safety, traffic management is particularly important and relevant departments have the practical application of value, are increasingly subject to the people's attention.
This paper uses image processing technology, pattern recognition technology, the vehicle registration positioning technology, character segmentation techniques and neural network technology to solve the problem of vehicle licence identification. Divided into image preprocessing, regional location and license plate geometric correction, character segmentation and normalized and Character Recognition of the four modules for real-time analysis of License Plate Recognition.
First papers on image preprocessing, will be collected on the plate containing the image of the gray, gray stretch, histogram equalization, filtering and image binarization handling, and a binarization based on the good results of the histogram best overall average threshold value; Then on China's plate features and common license plates in-depth study of positioning technology, a license plate based on regional distribution and gray geometric characteristics of the real-time positioning methods plate through the plate geometry deformation occurred and the reasons for the type analysis of the slope is based on the license plate tilt correction method; Based on the common character segmentation methods and the structural characteristics of its own license plate and a priori knowledge of the analysis, a projection based on a priori knowledge of the plate and the combination of segmentation method used to neighbouring interpolation method, although the relative accuracy But to meet the low system requirements and easy, characters will be normalized into the lattice, character recognition prepared. Finally, the PSO algorithm and the related principles of neural networks in brief and analytical studies, based on this highlights the PSO-BP neural network for real-time and its license plate character recognition.
本文运用图像处理技术、模式识别技术、车牌定位技术、字符分割技术、神经网络识别技术等来解决车辆牌照识别问题;分为图像预处理、车牌区域定位与几何校正、字符分割与归一化及字符识别四个模块对实时车牌识别进行分析研究。
论文首先研究图像的预处理,将采集到的含有车牌的图像进行灰度化、灰度拉伸、直方图均衡化、中值滤波和图像二值化处理,并改进了一种二值化效果较好的基于灰度直方图的全局最佳平均阈值法;然后对我国车牌特征和常见的车牌定位技术进行深入研究,改进了一种基于车牌区域灰度分布与几何特征的实时车牌定位方法;通过对车牌发生几何形变的原因及类型的分析研究,采用了基于斜率的车牌倾斜校正方法;基于常见的字符分割方法和车牌本身的结构特点及先验知识的分析研究,改进一种基于投影法与车牌先验知识相结合的分割方法;采用邻近插值法,虽然精确度相对较低但可以满足系统的要求且实现方便,将字符归一化为32×16的点阵,为字符的识别做好准备。最后对粒子群优化算法和神经网络的相关原理作了简要介绍和分析研究,在此基础上提出了PSO-BP神经网络并将其用于实时车牌字符识别。
Intelligent Transport System in the 21st century modernization of the transportation system an important development direction, it is a kind of information, intelligence, and a new type of modern society of the transport system. With the continued socio-economic development and increasing the living standards of the people, the entire community of transport demand growing, Intelligent Transportation System Application inevitable. Real-Time License Plate Recognition as a branch of Intelligent Transportation System ,in the large car park management system, public safety, traffic management is particularly important and relevant departments have the practical application of value, are increasingly subject to the people's attention.
This paper uses image processing technology, pattern recognition technology, the vehicle registration positioning technology, character segmentation techniques and neural network technology to solve the problem of vehicle licence identification. Divided into image preprocessing, regional location and license plate geometric correction, character segmentation and normalized and Character Recognition of the four modules for real-time analysis of License Plate Recognition.
First papers on image preprocessing, will be collected on the plate containing the image of the gray, gray stretch, histogram equalization, filtering and image binarization handling, and a binarization based on the good results of the histogram best overall average threshold value; Then on China's plate features and common license plates in-depth study of positioning technology, a license plate based on regional distribution and gray geometric characteristics of the real-time positioning methods plate through the plate geometry deformation occurred and the reasons for the type analysis of the slope is based on the license plate tilt correction method; Based on the common character segmentation methods and the structural characteristics of its own license plate and a priori knowledge of the analysis, a projection based on a priori knowledge of the plate and the combination of segmentation method used to neighbouring interpolation method, although the relative accuracy But to meet the low system requirements and easy, characters will be normalized into the lattice, character recognition prepared. Finally, the PSO algorithm and the related principles of neural networks in brief and analytical studies, based on this highlights the PSO-BP neural network for real-time and its license plate character recognition.
引文
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[2]陈艳,何春明.智能交通系统应用现状及其存在问题分析[J].交通标准化,2007,(8).
[3]Rafael C.Gonzales Richard E.Woods等著阮秋琦,阮宁智等译[M].数字图像处理(第二版)电子工业出版社,2003.3.
[4]张宏林.VisualC++数字图像模式识别技术及工程实践[M].人民邮电出版社.2003年2月第1版.
[5]路小波,张光华.基于二值图像的车牌精确定位方法[J].东南大学学报(自然科学版),2005,35(6):972-974.
[6]朱虹等编著.数字图像处理基础[M].北京:科学出版社,2005.
[7]陶军.车牌识别技术研究与实现[D].南京:南京理工大学模式识别与智能系统,2004.
[8]Cheoman Wu,Lei Chan On,Chan Hon Weng et al.Amacao License Plate Recognition System.In:Proceeding of the Fourth International Conference on Machine Learning and Cybernetics,Guangzhou,2005,4506-4510.
[9]Bemsen J.Dynamic thresholding of gray-level images[A].Proc.8th Int.Conf.on Pattern Recognition[C].LOSAngeles:IEEE Computer Society Press,1986,1251-1255.
[10]邱刚,王养丽.基于边缘特征和神经网络的汽车牌照定位方法[J].微机发展,2005.430-32.
[11]付仲良,黄书强,陈氓,陈江平.货车图像车牌区快速定位及字符切割算法[J].计算机工程与设计,24(1):77-79,2003.
[12]朱风云,曹晓光等.机动车车牌自动识别系统与VMLA定位算法[J].中国图像图形学报,8(6):679-682,2003.
[13]Taleb-Ahmed,A.Hamad,D.Tilmant.Vehicle license plate recognition in marketing application Intelligent Vehicles Symposium.IEEE,9-11 June 2003 90-94.
[14]韩智广等.车牌分割与矫正[J].计算机工程与应用,2003.(9):210-212.
[15]AN Yong,ZHANG Gao-wei.A Gray-Image-Based License PlateRecognition System[J].Computer Engineering & Science,2006,28(2):61-65.
[16]高珊,刘万春,朱玉文.基于SVM的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.
[17]陈寅鹏,丁晓清.复杂车辆图像中的车牌定位与字符分割方法[J].红外与激光工程,2004,33(1):29-33.
[18]Danian Zheng,Yannan Zhao,Jiaxin Wang.An efficient method of license plate location[J].Pattern Recongnition Letters.2005,26(15):2431-2438.
[19]Sunghoon Kim,Daechul Kim,Younbok Ryu,and Gyeonghwan Kim.A robust license-plate-extraction method under complex image conditions[C].16th international conference on pattern recognition,2002:176-179.
[20]沈世宴,盛祤智.基于边缘检测的车牌图像分割技术[J].自动化技术与应用,2004,23(3):24-26.
[21]白洪亮,娄正良,邹明祸,刘吕平.复杂背景下基于形态学的车牌识别系统[J].公路交通科技,.2004,21(10):117-120.
[22]Bai Hongliang,Liu Changping.A hybrid license plate extraction method based on edge statistics and morphology[C].ICPR.2004:831-834.
[23]余棉水,黎绍发.基于边缘与SVM的车牌自动定位与提取[J].计算机应用研究,2004,21(10):131-133.
[24]曹刚,游志胜,赵树龙.一种基于自适应能量滤波的快速车牌定位方法[J].光电子·激光,2003,14(5):523-525.
[25]David Llorens,Andress Marzal,Vicente Palazbn,and Juan M.Vilar.Car license plate extraction and recognition based on connected components analysis and HMM Decoding [C].IbPRIA 2005,571-578,2005.
[26]Regis C.P.Marques,Fotima N.S.Medeiros,Jilseph Lopes Silva,and Cassius M.Laprano.License vehicle plates localization using Maximum Correlation[C].SSPR&SPR 2004,LNCS 3138,470-476,2004.
[27]蔡饮涛,方水良,任俊.基于边缘生长的车牌定位新方法[J].公路交通科技,2004,21(11):110-113.
[28]卢雅琴,邬凌超.基于数学形态学的车牌定位方法[J].计算机工程,2005,31(3):224-226.
[29]陈寅鹏,丁晓清.复杂车辆图像中的车牌定位与字符分割方法[J].红外与激光工程,2004,33(1):29-33.
[30]李文举,梁德群,张旗,樊鑫.基于边缘颜色对的车牌定位新方法[J].计算机学报,2004,27(2):204-208.
[31]Xifan Shi,Weizhong Zhao,and Yonghang Shen.Automatic license plate recognition system based on color image processing[C].ICCSA 2005,LNCS 3483,1159-1168,2005.
[32]高朝晖,黄卫.基于彩色图像车牌分割研究[J].公路交通科技,2004,21(8):114-117.
[33]许剑峰,黎绍发.车牌识别中的颜色分析[J].计算机工程与应用,2004,40(25):230-232.
[34]杨枝灵,王开等.Visual C++数字图像获取处理及实践应用(第一版)[M].北京:人民邮电出版社,2003,174-175.
[35]樊孝宏,戚飞虎.一种基于纹理和颜色综合特征的车牌定位新方法[J].计算机工程,2004,30(13):125-127.
[36]Suryanarayana P V,Suman K Mitra,Asim Banerjee,et al.A Morphology Based Approach for Car License Plate Extraction[C].Chennai,IndiaL:IEEE,2005.
[37]LI G,ZENG R L,Lin L.Research on vehicle license plate location based on neural networks C.Proceedings of IEEE First International Conference on Innovative Computing,Information and Control,2006:174-177.
[38]金一粟,袁宝民,于万波,魏小鹏.基于分形盒子维数的车牌定位方法[J].计算机应用研究,9:40-41,44.2002.
[39]李王君,刘莉.高效快速车牌定位算法研究[J].甘肃科学学报,2006,18(4):88-91.
[40]吴传孙,汽车牌照自动识别技术研究[D].硕士学位论文.江西师范大学,2003.
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