基于数学形态学的车牌自动识别
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摘要
随着汽车行业和公路交通事业的迅速发展,交通管理的智能化、信息化是大势所趋。车牌自动识别系统(LPR)是智能交通系统(ITS)的核心,是交通信息服务系统的重要组成部分,也是整个智能系统顺利运作的关键和基础。
车牌自动识别包括:车牌定位、字符分割和字符识别。虽然数学形态学已被应用于车牌定位,但在字符分割方面应用较少。本文探讨了将数学形态学与其它方法相结合,改善车牌定位、字符分割的性能,从而得到较高的车牌识别率。在车牌定位方面,使用了形态学和拓扑学相结合的方法对车牌区域进行定位提取。该方法首先采用Top-Hat变换以及开、闭运算对抓拍的车辆图像进行预处理;然后采用连通体态分析法(CCA)对图像进行粗定位;最后对计算得到的车牌候选区的欧拉数进行判别,最终提取真正的车牌区域。在字符分割方面,采用数学形态学与投影相结合的方法进行车牌字符分割。首先采用数学形态学突出车牌字符区域特征,然后利用水平投影除去上下边界,垂直投影突出单个字符区域,再结合车牌固有特征等先验知识最终分割出字符。在字符识别方面,采用了模板匹配方法。先将字符归一化,然后提取字符的过线特征、左右轮廓特征,根据这些特征组成字符的特征向量,对字符进行初分类,然后利用模板匹配的方法对字符进行细分类,从而完成字符的识别。文中基于数学形态学的车牌识别方法定位准确度较高,字符分割质量较好,而且算法实现简单,对提高整个系统的时效性有很大帮助。
最后利用SOPC搭建车牌自动识别系统的FPGA硬件平台,并利用NIOS II软处理核实现了识别的软件设计。
With the rapid growth of the automobile enterprise and transpotation, the world requires the intelligent management of traffic management. License Plate Recognition system (LPR) is the core of Intelligent Traffic System (ITS). It is the main part of the traffic information service system and plays an important role in the intelligent control system.
The license plate recognition is composed of three steps, i.e., license plate location, character segmentation, and character recognition. Although mathematical morphology has been employed in license plate location, it has not been used in other two steps. In this paper, mathematical morphology and other techniques are used in license plate location and character segmentation to improve their performance and the license plate recognition rate is therefore significantly increased. At the stage of license plate location, Morphology and Topology are employed in plate location. Firstly, image preprocessing is adopted, which is implemented by Top-Hat transform, opening and closing operations. Secondly, connected components analysis is performed to detect candidate regions. At last, the real license plate area is obtained according to the Euler Number of candidate regions. During the stage of character segmentation, improved projection algorithm based on Morphology and Projection is used in segmentation. Firstly, morphology is adopted to intensify the whole characters area. Secondly, horizontal projection is performed to eliminate horizontal plate frames and vertical projection is used to intensify the single character area. At last, every character is segmented according to the characteristic features of the plate. In the character recognition stage, template matching method is used in recognition. Characters should be normalized at first. And then we abstract the characters’thread attribute, periphery attribute, and formed a vector, and compare it with the attribute vectors in character template library, so the characters are recognised. This LPR algorithm based on Mathematical Morphology has good accuracy and time effectiveness.
At last, a hardware of license plate recognition system based on FPGA is designed by SOPC, and the software design of the system is accomplished by Nios II soft core processor.
车牌自动识别包括:车牌定位、字符分割和字符识别。虽然数学形态学已被应用于车牌定位,但在字符分割方面应用较少。本文探讨了将数学形态学与其它方法相结合,改善车牌定位、字符分割的性能,从而得到较高的车牌识别率。在车牌定位方面,使用了形态学和拓扑学相结合的方法对车牌区域进行定位提取。该方法首先采用Top-Hat变换以及开、闭运算对抓拍的车辆图像进行预处理;然后采用连通体态分析法(CCA)对图像进行粗定位;最后对计算得到的车牌候选区的欧拉数进行判别,最终提取真正的车牌区域。在字符分割方面,采用数学形态学与投影相结合的方法进行车牌字符分割。首先采用数学形态学突出车牌字符区域特征,然后利用水平投影除去上下边界,垂直投影突出单个字符区域,再结合车牌固有特征等先验知识最终分割出字符。在字符识别方面,采用了模板匹配方法。先将字符归一化,然后提取字符的过线特征、左右轮廓特征,根据这些特征组成字符的特征向量,对字符进行初分类,然后利用模板匹配的方法对字符进行细分类,从而完成字符的识别。文中基于数学形态学的车牌识别方法定位准确度较高,字符分割质量较好,而且算法实现简单,对提高整个系统的时效性有很大帮助。
最后利用SOPC搭建车牌自动识别系统的FPGA硬件平台,并利用NIOS II软处理核实现了识别的软件设计。
With the rapid growth of the automobile enterprise and transpotation, the world requires the intelligent management of traffic management. License Plate Recognition system (LPR) is the core of Intelligent Traffic System (ITS). It is the main part of the traffic information service system and plays an important role in the intelligent control system.
The license plate recognition is composed of three steps, i.e., license plate location, character segmentation, and character recognition. Although mathematical morphology has been employed in license plate location, it has not been used in other two steps. In this paper, mathematical morphology and other techniques are used in license plate location and character segmentation to improve their performance and the license plate recognition rate is therefore significantly increased. At the stage of license plate location, Morphology and Topology are employed in plate location. Firstly, image preprocessing is adopted, which is implemented by Top-Hat transform, opening and closing operations. Secondly, connected components analysis is performed to detect candidate regions. At last, the real license plate area is obtained according to the Euler Number of candidate regions. During the stage of character segmentation, improved projection algorithm based on Morphology and Projection is used in segmentation. Firstly, morphology is adopted to intensify the whole characters area. Secondly, horizontal projection is performed to eliminate horizontal plate frames and vertical projection is used to intensify the single character area. At last, every character is segmented according to the characteristic features of the plate. In the character recognition stage, template matching method is used in recognition. Characters should be normalized at first. And then we abstract the characters’thread attribute, periphery attribute, and formed a vector, and compare it with the attribute vectors in character template library, so the characters are recognised. This LPR algorithm based on Mathematical Morphology has good accuracy and time effectiveness.
At last, a hardware of license plate recognition system based on FPGA is designed by SOPC, and the software design of the system is accomplished by Nios II soft core processor.
引文
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[2] Brugge M. H.ter, Stevens J. H., Nijhuis J. A. G., and Spaanenburg L. License plate recognition using DTCNNs [J]. IEEE Int. Workshop Cellular Neural Netw and Appl, 1998, (5): 212–217.
[3] Nijhuis J.A.G., Brugge M.II.ter., Helmholt K.A. etc. Car License Plate Recognition with Neural Networks and Fuzzy Logic [J]. IEEE International Conference on Publication, 1998, (8): 11-14.
[4] Kim K. K., Kim K. I., Kim J. B., and Kim H. J. Learning-based approach, for license plate recognition [J]. IEEE Signal Process. Soc.Workshop, Neural Netw. Signal Process, 2000, (2): 614–623.
[5] Shyang-Lih Chang, Li-Shien Chen, Yun-Chung, and Sei-Wan Chen. Automatic License Plate Recognition [J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 5(1): 42-52.
[6] Parisi R., Claudio E.D.Di. Car plate recognition by neural networks and image processing [J]. IEEE Trans. On Vehicular Technology, 1998, 44(4) : 790-799.
[7] Eun Ryung Lee, Pyeoung Kee King. Automatic recognition of a car license plate using color image processing [J]. Journal of Korea Institute of Telematics and Electronics, 1995, 24(2) : 128-131.
[8] Takashi Naito, Toshihiko Tsukada. Moving-Vehicle license plate recognition method robust to changes in lighting conditions [J]. System and Computer in Japan, 2000, 31(11): 82-92.
[9] Masataka Kagesawa, Shinichi Ueno. Vehicle recongnition system in infrared images using IMAP parallel vision board [J]. Electronics and Communications in Japan, 2003, 86(3), No.5.
[10] Thanongsak, Sirithinaphong. Extracting of car license plate using motor vehicle regulation and character pattern recognition [J]. IEEE Trans Electronic Computers, 1998, (15): 559-563.
[11]廖金周,宣国荣.车辆牌照的自动分割[J].微型电脑应用, 1999, 7(11): 32～34.
[12]刘智勇,刘迎建.车牌识别中的图象提取及分割[J].中文信息学报, 2000, 14(4) : 26-29.
[13]胡爱明,周孝宽.车牌图像的快速匹配识别方法[J].计算机工程与应用, 2003, (7): 90-91.
[14]洛雪超,刘桂雄,冯云庆.一种基于车牌特征信息的车牌识别方法[J].华南理工大学学报, 2003, (4): 70-73.
[15]袁宝民,于万波,魏小鹏.汽车牌照定位研究综述[J].大连大学学报, 2002, (4): 6-11.
[16]冯文毅.光电混合体全息子波关联存储车辆牌照识别系统[J].红外与毫米波学报, 1999, 6(11): 472-478.
[17]张引,潘云鹤.彩色汽车图象牌照定位新方法[J].中国图形图像学报, 2001, 6(4) : 32-38.
[18]章东平,刘济林,罗义军.车辆牌照字符的提取[J].电路与系统学报, 2003, 8(4): 73-76.
[19]刘庆祥,蒋天发.车牌图象定位方法的研究与应用[J].计算机工程与应用, 2007, 43(24): 233-234.
[20]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, (9): 210-212.
[21]黄志斌,陈锻生.支持向量机在车牌字符识别中的应用[J].计算机工程, 2003, 29(5): 192-194.
[22]汤井田,黄国祥,朱德兵.一种基于Radon变换的车牌图像分割和识别方法[J].中南工业大学学报, 2002, 33(1): 5-7.
[23]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): 285-288.
[24]刘效静,成瑜.汽车牌照自动识别技术研究[J].南京航空航天大学学报, 1998, 30(5): 12-17.
[25]付仲良,黄书强,陈珉等.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, 2003, 24(1): 77-79.
[26]朱风云,曹晓光,Ahmed Hassanien等.机动车车辆自动识别系统与VMLA定位算法[J].中国图象图形学报, 2003, 8(6): 679-682.
[27]李文举,梁德群,张琪等.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.
[28]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.
[29]杨国来,林男,姬孝斌等.基于数学形态学和综合颜色特征的车牌定位方法[J].甘肃科学学报, 2007, 19(1): 114-117.
[30]攀孝宏,戚飞虎.一种基于纹理和颜色特征的车牌定位新方法[J].计算机工程, 2004, 30(13): 125-127.
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