基于图像处理和神经网络的车牌识别系统研究
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摘要
车牌识别系统(LPR)是智能交通系统(ITS)的核心组成部分,在现代交通收费管理系统中发挥着举足轻重的作用。近年来,对车牌识别系统的研究已经成为一个热点问题。
论文首先分析车牌识别系统技术的研究现状,对系统中的图像预处理、车牌定位、字符分割和识别四大重点模块进行全面论述;对字符识别技术进行深入研究,设计并实现了车牌识别系统。为了提高系统的识别率和鲁棒性,仔细分析四个模块设计中的重点和难点,并提出解决方案。
在预处理模块设计中,本文结合多种图像处理技术,采用模糊c均值二值化、形态学滤波和Sobel算子边缘检测等预处理算法。比较研究了基于特征统计、改进Sobel算子边缘检测、粒子图像相关法的车牌定位方法,并通过实验证明粒子图像相关法是设计本系统的最佳优化方案。在车牌的字符分割模块中,提出一种基于多尺度模板匹配的分割算法,利用尺度变换的方法找到车牌区域全局最优模板匹配信息,结合Hough变换达到更好的分割效果;为有效剔除字符图像中的噪声区域,结合预处理阶段最大连通域的边界均值方法来解决,使得此算法具有很好的鲁棒性。参照目前的研究现状重点分析BP神经网络,提出一套附加动量法改进BP算法的设计方案,通过对比实验,验证了改进算法方案的有效性。
实验证明,本文所采用的方法能达到较好的识别效果,具有一定的高效性、鲁棒性和实时性。
License Plate Recognition System (LPR) is the core component of Intelligent Transportation Systems (ITS). LPR plays an important role in a modern traffic management system. In recent years, the research on the license plate recognition system has become a hot issue.
First of all, the paper introduces the research status on license Plate recognition system, and carry out a comprehensive exposition on four key modules about image Pre-processing, license plate location, character segmentation and identification.Give an in-depth study of character recognition technology, design and achieve a license plate recognition system. In order to improve the recognition accuracy and robustness, give a careful analysis of focus and difficult problems which are encountered in the course of four modular designs, and propose some solution.
In the design of pre-processing module, use a variety of image processing technology and adopt a series of pre-processing algorithm including fuzzy c-means binarization, Morphology filtering,Sobel edge detection operator.The paper study and compare the methods of the feature-based statistics, the improved sobel edge detection, particle image correlation method, and experiments prove: particle image correlation method is the most good optimization program.In the license plate character segmentation module, present a template matching method which based on multiscale segmentation algorithm, the use of scaling methods to find the global optimum plate region template matching information, combining the Hough transform this algorithm has good robustness. In the light of the current study, analyze the status and quote BP networks, give a set of additional momentum method to improve the BP algorithm. The comparing experiments verify the effectiveness of the improved algorithm.
Experiments show that the methods in this paper achieve better recognition results, with a certain degree of efficiency, robustness and real-time.
论文首先分析车牌识别系统技术的研究现状,对系统中的图像预处理、车牌定位、字符分割和识别四大重点模块进行全面论述;对字符识别技术进行深入研究,设计并实现了车牌识别系统。为了提高系统的识别率和鲁棒性,仔细分析四个模块设计中的重点和难点,并提出解决方案。
在预处理模块设计中,本文结合多种图像处理技术,采用模糊c均值二值化、形态学滤波和Sobel算子边缘检测等预处理算法。比较研究了基于特征统计、改进Sobel算子边缘检测、粒子图像相关法的车牌定位方法,并通过实验证明粒子图像相关法是设计本系统的最佳优化方案。在车牌的字符分割模块中,提出一种基于多尺度模板匹配的分割算法,利用尺度变换的方法找到车牌区域全局最优模板匹配信息,结合Hough变换达到更好的分割效果;为有效剔除字符图像中的噪声区域,结合预处理阶段最大连通域的边界均值方法来解决,使得此算法具有很好的鲁棒性。参照目前的研究现状重点分析BP神经网络,提出一套附加动量法改进BP算法的设计方案,通过对比实验,验证了改进算法方案的有效性。
实验证明,本文所采用的方法能达到较好的识别效果,具有一定的高效性、鲁棒性和实时性。
License Plate Recognition System (LPR) is the core component of Intelligent Transportation Systems (ITS). LPR plays an important role in a modern traffic management system. In recent years, the research on the license plate recognition system has become a hot issue.
First of all, the paper introduces the research status on license Plate recognition system, and carry out a comprehensive exposition on four key modules about image Pre-processing, license plate location, character segmentation and identification.Give an in-depth study of character recognition technology, design and achieve a license plate recognition system. In order to improve the recognition accuracy and robustness, give a careful analysis of focus and difficult problems which are encountered in the course of four modular designs, and propose some solution.
In the design of pre-processing module, use a variety of image processing technology and adopt a series of pre-processing algorithm including fuzzy c-means binarization, Morphology filtering,Sobel edge detection operator.The paper study and compare the methods of the feature-based statistics, the improved sobel edge detection, particle image correlation method, and experiments prove: particle image correlation method is the most good optimization program.In the license plate character segmentation module, present a template matching method which based on multiscale segmentation algorithm, the use of scaling methods to find the global optimum plate region template matching information, combining the Hough transform this algorithm has good robustness. In the light of the current study, analyze the status and quote BP networks, give a set of additional momentum method to improve the BP algorithm. The comparing experiments verify the effectiveness of the improved algorithm.
Experiments show that the methods in this paper achieve better recognition results, with a certain degree of efficiency, robustness and real-time.
引文
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