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上海交通大学硕士学位论文 摘要
I
基于 MATLAB 的车牌识别系统研究
摘 要
近几年,车牌识别系统作为智能交通的一个重要方向越来越受到重
视。车牌识别系统可以应用于停车场管理系统、高速公路超速管理系统、
城市十字路口的“电子警察”、小区车辆管理系统等各个领域,对国家
的安全发展有很大的作用。虽然目前已有一些车牌识别系统相关产品出
现,但是对其算法的研究发展从没有停止,仍有许多学者在做着进一步
的研究改进。
本文首先对车牌识别系统的现状和已有的技术进行了深入的研究,
在研究的基础上开发出一个基于 MATLAB 的车牌识别系统。确定了整
体设计方案,其中软件部分包括车牌定位、车牌字符切分及车牌字符识
别三个模块。车牌定位模块中提出了基于小波变换的车牌边缘提取的算
法,以及车牌二次定位的算法,提高了系统在光照条件较差的情况下的
定位准确率,该算法对于各种底色的车牌具有良好的适应性;车牌的二
值化采用了改进的 Otus 算法,重新划分了其两维直方图的区域,改进
后的算法大大减少了运行时间,对于各种类型的车牌都能达到较好的二
值化效果;针对 BP 神经网络字符识别算法,采用有动量的梯度下降法
训练网络,减小了神经网络学习过程的振荡趋势,使得 BP 网络能够较
快的达到收敛,完成车牌字符的识别。对模板匹配算法和 BP 网络算法
进行对比,证明了 BP 网络算法要优于模板匹配算法。
根据上述算法搭建了一个测试平台。整个测试平台的软件部分采用
MATLAB 的 M 语言编写。通过测试平台,对 353 幅卡口汽车照片进行
车牌识别,测试系统的性能。测试结果表明,本课题设计的车牌识别系
统可有效地实现车牌识别,为今后的产品化奠定了很好的技术基础。
关键词: 车牌识别,小波变换,Otsu 算法,模板匹配,BP 网络,MATLAB
上海交通大学硕士学位论文 ABSTRACT
II
RESEARCH ON PLATE LICENSE
RECOGNITION SYSTEM BASED ON MATLAB
ABSTRACT
In recent years, the development of intelligent transportation has
become more and more important. As an important aspect in intelligent
transportation, plate license recognition system has taken more and more
attention. The plate license recognition system can be applied to public
parking, highway speeding management system, crossing road, district
vehicle management system, and so on. Although now there are already
some exsiting plate lecense recognition systems, the research and
development of arithmetic have never stopped, and there are still many
scholars who are doing further research and improvement.
Firstly, the paper gives a deep research on the status and technique of
the plate license recognition system. On the basis of research, a solution of
plate license recognition system is proposed, and the paper focused on the
software part. The whole system concludes three modules. They are plate
location, plate character segmentation, and plate character recognition. In
the plate location module, the paper puts forward an arithmetic of plate edge
recognition by wavelet decomposition, and an arithmetic of locating twice,
which improve the accuracy in bad light condition, and are fit for plates with
different grounding. An improved Otsu arithmetic is used in the process of
binaryzating, which reduces the running time, and can achieve good effect
for different kinds of plate. In character recognition part, with the
momentum of the gradient descent method, the BP neural network can fast
convergence.Compared the BP neural network with template matching
arithmetic, which improves that the BP neural network are better than the
template matching arithmetic.
上海交通大学硕士学位论文 ABSTRACT
III
Then, a test platform has been built with MATLAB, for the test of the
system. Through the test of 353 monitoring car photographs, the results
shows that the system can effectively meets the requirement, and lay a good
foundation of technology for productization.
KEY WORDS: plate license recognition, wavelet transform, Otsu, template
matching, BP neural network, MATLAB
上海交通大学硕士学位论文 图目录
VII
图目录
图 1 车牌识别系统 ····································································································1
图 2 自选号牌车牌示例·····························································································3
图 3 车辆牌照识别系统结构图···············································································10
图 4 系统流程图 ······································································································13
图 5 车牌定位的过程 ······························································································15
图 6(a)原始汽车图像 (b)灰度图 ···································································16
图 7 灰度变换的对比曲线·······················································································17
图 8(a)灰度图 (b)灰度变换后的图像 ···························································17
图 9(a)灰度图 (b)中值滤波后的图像 ···························································18
图 10 小波分解树
[10]
································································································21
图 11 小波变换的 Mallat 算法 ················································································23
图 12 二维小波变换的 Mallat 算法 ········································································24
图 13 车辆灰度图 ····································································································25
图 14 X=214 数据线的灰度图··················································································25
图 15 用 HAAR 小波进行五层分解········································································26
图 16 车牌图像的小波分解·····················································································27
图 17 小波分解提取边缘··························································································27
图 18 开闭运算后的图像·························································································28
图 19 车牌区域标记 ································································································29
图 20 初步提取的车牌 ····························································································29
图 22 平滑后的水平差分累加投影图·····································································31
图 23 水平定位后的图像·························································································31
图 24 平滑后的垂直差分累加投影图·····································································32
图 25 精确定位后的车牌·························································································32
图 26 车牌定位算法 ································································································33
图 27 车牌字符切分流程·························································································35
图 28 二维 Otsu 算法阈值求解示意图 ···································································38
图 29 改进的 Otsu 算法阈值求解示意图 ·······························································40
上海交通大学硕士学位论文 图目录
VIII
图 30 改进的 Otsu 算法二值化实验 ·······································································41
图 31 图像空间 ········································································································42
图 32 Hough 空间 ·····································································································42
图 33 利用 Hough 变换查找倾斜角度 ····································································43
图 34 车牌二值子图及其水平投影·········································································44
图 35 坐标变换示意图 ····························································································46
图 36 两种校正算法的比较·····················································································47
图 37 字符投影图 ····································································································48
图 38 字符切分后的效果图·····················································································49
图 39 车牌字符切分算法·························································································50
图 40 加权后模板与原模板·····················································································54
图 41 特征提取 ········································································································54
图 42 模板匹配字符识别流程图·············································································56
图 43 BP 神经网络结构示意图················································································58
图 44 三层 BP 网络示意图······················································································58
图 45 BP 网络的应用过程························································································60
图 46 BP 算法流程 ···································································································63
图 47 车牌识别测试系统流程·················································································65
图 48 车牌识别测试系统界面·················································································67
图 49 测试系统文件菜单·························································································68
图 50 测试系统视频设备菜单·················································································68
图 51 测试系统识别参数设置菜单·········································································69
图 52 测试系统系统设置菜单·················································································70
图 53 测试分析(1) ······························································································71
图 54 测试分析(2) ······························································································72
图 55 测试分析(3) ······························································································73
图 56 测试分析(4) ······························································································73
图 57 测试分析(5) ······························································································74
图 58 测试分析(6) ······························································································75
图 59 测试分析(7) ······························································································76
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