上海交通大学硕士学位论文
摘要
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。
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 pared the BP neural network with template matching arithmetic, which improves that the BP neural network are better than the template matching arithmetic.
KEY WORDS: plate license recognition, wavelet transform, Otsu, etwork, MATLAB
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上海交通大学硕士学位论文
图目录
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
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上海交通大学硕士学位论文
ABSTRACT
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.