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基于神经网络和支持矢量机的多机动车车牌在线检测方法

刘进博 朱新新 伍越 杨凯 陈卫

刘进博, 朱新新, 伍越, 杨凯, 陈卫.基于神经网络和支持矢量机的多机动车车牌在线检测方法.自动化学报, 2021, 47(2): 316-326 doi: 10.16383/j.aas.c180753
引用本文: 刘进博, 朱新新, 伍越, 杨凯, 陈卫.基于神经网络和支持矢量机的多机动车车牌在线检测方法.自动化学报, 2021, 47(2): 316-326 doi: 10.16383/j.aas.c180753
Liu Jin-Bo, Zhu Xin-Xin, Wu Yue, Yang Kai, Chen Wei. An on-line method for multi-license plates recognition based on neural network and support vector machine. Acta Automatica Sinica, 2021, 47(2): 316-326 doi: 10.16383/j.aas.c180753
Citation: Liu Jin-Bo, Zhu Xin-Xin, Wu Yue, Yang Kai, Chen Wei. An on-line method for multi-license plates recognition based on neural network and support vector machine. Acta Automatica Sinica, 2021, 47(2): 316-326 doi: 10.16383/j.aas.c180753

基于神经网络和支持矢量机的多机动车车牌在线检测方法

doi: 10.16383/j.aas.c180753
基金项目: 

国家自然科学基金 11802321

详细信息
    作者简介:

    刘进博  中国空气动力研究与发展中心超高速空气动力研究所助理研究员. 2016年获得国防科技大学航空宇航科学与技术专业博士学位.主要研究方向为计算机视觉.E-mail: liujinbo2088@163.com

    伍越  中国空气动力研究与发展中心超高速空气动力研究所助理研究员. 2015年获得国防科技大学硕士学位.主要研究方向为信号处理. E-mail: wuyue_cardc@163.com

    杨凯  中国空气动力研究与发展中心超高速空气动力研究所助理研究员. 2014年获得哈尔滨工业大学博士学位.主要研究方向为振动信号处理和高频脉动热流测试. E-mail: yg.hit@hotmail.com

    陈卫  中国空气动力研究与发展中心超高速空气动力研究所助理研究员. 2015年获得国防科技大学博士学位.主要研究方向为光学测量. E-mail: chenweikeeping@163.com

    通讯作者:

    朱新新  中国空气动力研究与发展中心超高速空气动力研究所助理研究员. 2013年获得国防科技大学硕士学位, 主要研究方向为气动热与热防护试验测试技术.本文通信作者. E-mail: xinxincomplex@126.com

  • 本文责任编委 金连文

An On-line Method for Multi-license Plates Recognition Based on Neural Network and Support Vector Machine

Funds: 

National Natural Science Foundation of China 11802321

More Information
    Author Bio:

    LIU Jin-Bo    Assistant researcher at Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center. He received his Ph. D. degree in aeronautical and astronautical science and technology from National University of Defense Technology in 2016. His main research interest is computer vision

    WU Yue   Assistant researcher at Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center. He received his master degree from National University of Defense Technology in 2015. His main research interest is signal processing

    YANG Kai   Assistant researcher at Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center. He received his Ph. D. degree from Harbin Institute of Technology in 2014. His research interest covers vibration signal processing and high-frequency heat-flux measurement

    CHEN Wei   Assistant researcher at Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center. He received his Ph. D. degree from National University of Defense Technology in 2015. His main research interest is optical measurement

    Corresponding author: ZHU Xin-Xin   Assistant researcher at Hypervelocity Aerodynamics Institute, China Aerodynamics Research and Development Center. He received his master degree from National University of Defense Technology in 2013. His research interest covers measuring and testing technique in aerothermodynamics and thermal protection. Corresponding author of this paper
  • Recommended by Associate Editor JIN Lian-Wen
  • 摘要: 针对道路交通多车牌识别问题, 提出了一种快速鲁棒的多车牌检测识别方法, 包括多车牌检测和车牌字符识别两部分:构造BP (Back-Propagation)神经网络模型用于颜色识别, 结合图像形态学运算方法, 筛选候选车牌目标, 基于支持矢量机从候选车牌目标中判别真正的车牌目标; 通过轮廓尺寸判断, 并结合车牌尺寸特征, 依次分割提取城市代码字符块、省份代码字符块及5位机动车编码字符块, 最后基于BP神经网络识别字符块内容.基于上述原理, 开发了鲁棒的多机动车车牌自动检测识别系统, 并在真实场景中进行了实验测试, 结果表明: 1)车辆在正常速度行驶条件下, 系统依然可以保证90%以上的车牌检测识别正确率; 2)系统可实现同时多车牌检测识别; 3)文中实验硬件配置下, 系统单幅图像检测识别平均时间低于130 ms, 处理频率约8 Hz.
    Recommended by Associate Editor JIN Lian-Wen
    1)  本文责任编委 金连文
  • 图  1  颜色二值化

    Fig.  1  Image binarization

    图  2  颜色识别原理

    Fig.  2  Principle of color identification

    图  3  中值滤波

    Fig.  3  Median filter

    图  4  形态学运算

    Fig.  4  Morphology operation

    图  5  目标轮廓查找

    Fig.  5  Find contours of targets

    图  6  基于支持矢量机的车牌判别原理示意图

    Fig.  6  Distinguish real plates based on SVM

    图  7  输入特征生成

    Fig.  7  Input feature

    图  8  车牌图像二值化

    Fig.  8  License plate binary image

    图  9  斑点检测效果

    Fig.  9  Spot detection

    图  10  候选字符块筛选及尺寸判断

    Fig.  10  Filter of candidate character block and size judgement

    图  11  字符分割提取

    Fig.  11  Character segment

    图  12  基于BP神经网络的字符识别原理

    Fig.  12  Character recognition based on BP neural network

    图  13  第一组实验部分结果显示

    Fig.  13  Partial results of the first experiment

    图  14  第二组实验部分结果显示

    Fig.  14  Partial results of the second experiment

    图  15  第三组实验部分结果显示

    Fig.  15  Partial results of the third experiment

    表  1  BP神经网络训练参数设置

    Table  1  Training parameters of BP neural network

    $Mat\ layers(1, \ 3, \ CV\_\, 32SC1);$
    $layers.at \langle int\rangle (0) = 7;$ //输入层单元数量
    $layers.at \langle int\rangle (1) = 11;$ //隐藏层神经元数量
    $layers.at \langle int\rangle (2) = 5;$ //输出层单元数量
    $CvANN\_\, MLP\_\, TrainParams \ params;$ //参数
    $params.train\_\, method \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;= \\CvANN\_\, MLP\_\, TrainParams\;\;::\\BACKPROP; $ //训练方法
    $params.bp\_\, moment\_\, scale = 0.1; $ //动力项因子
    $params.bp\_\, dw\_\, scale = 0.1; $ //梯度项因子
    下载: 导出CSV

    表  2  候选车牌筛选算法

    Table  2  Filter of candidate plates

    $Require: contours$ //为查找到的目标轮廓集合
    $Require: rect\_\, $ //为轮廓对应的最小外接矩
    $Require: minAreaRect$ //为用于计算最小外接矩的函数
    $Require: thresh\_\, min$ //为车牌长宽比判断阈值下界
    $Require: thresh\_\, max$ //为车牌长宽比判断阈值上界
    $Require: k$ //为查找到的目标轮廓数
    $Require: recsults$ //为经过尺寸筛选后得到的候选车牌集合
    for $i$ from 1 to $k$ do
    $rect\_\, \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;=\\ minAreaRect(contours[i])$
    $if\; rect\_\, .height / rect\_\, .width > \\thresh\_\, min \&\& rect\_\, .height / rect\_\, .width <$
    $ thresh\_\, max do$
    $results$←$rect$
    end if
    end for
    Return results
    下载: 导出CSV

    表  3  SVM训练参数设置

    Table  3  Training parameters of SVM

    $CvSVMParams\ SVM\_\, params;$
    $SVM\_\, params.svm\_\, type \;\;= \\CvSVM::C\_\, SVC;$
    $SVM\_\, params.kernel\_\, type \;\;= \\CvSVM::RBF;$ //CvSVM::RBF径向基函数, 也就是高斯核
    $SVM\_\, params.degree = 0.1;$ //内核函数参数
    $SVM\_\, params.gamma = 1;$ //内核函数参数
    $SVM\_\, params.coef0 = 0.1;$ //内核函数参数
    $SVM\_\, params.C = 1;$ //SVM类型参数
    $SVM\_\, params.nu = 0.1;$ //SVM类型参数
    $SVM\_\, params.p = 0.1;$ //SVM类型参数
    下载: 导出CSV

    表  4  BP神经网络训练参数设置

    Table  4  Training parameters of BP neural network

    $Mat\ layers(1, \ 3, \ CV\_\, 32SC1);$
    $layers.at\langle int\rangle(0) = 173;$ //输入层单元数量
    $layers.at\langle int\rangle(1) = 41;$ //隐藏层神经元数量
    $layers.at\langle int\rangle (2) = 65;$ //输出层单元数量
    $CvANN\_\, MLP\_\, TrainParams \ params;$ //参数
    $params.train\_\, method \;\;\;\;\;\;\;\;\;= \\CvANN\_\, MLP\_\, TrainParams\;\;\;::\\BACKPROP;$ //训练方法
    $params.bp\_\, moment\_\, scale = 0.1;$ //动力项因子
    $params.bp\_\, dw\_\, scale = 0.1;$ //梯度项因子
    下载: 导出CSV
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  • 收稿日期:  2018-11-12
  • 录用日期:  2019-04-15
  • 刊出日期:  2021-02-26

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