在我們的日常生活中,客運(yùn)和貨運(yùn)都離不開車輛。自動(dòng)車牌分割與識(shí)別（AVLPSR）技術(shù)是智能交通系統(tǒng)（ITS）的關(guān)鍵技術(shù)之一。目前,AVLPSR技術(shù)已廣泛應(yīng)用于停車場(chǎng)、自動(dòng)收費(fèi)、門禁、交通執(zhí)法、過境管制、交通監(jiān)控等多種現(xiàn)實(shí)應(yīng)用場(chǎng)景,并且在各個(gè)領(lǐng)域都取得了良好的效果。中國(guó)標(biāo)準(zhǔn)車牌有多種類型,車牌的構(gòu)成涉及38個(gè)漢字、24個(gè)大寫英文字母（字母O和I除外）和1 0個(gè)數(shù)字（臨時(shí)車牌除外）。AVLPSR方法可以利用圖像處理技術(shù)車輛圖像來檢測(cè)車牌信息。在AVLPSR技術(shù)中,最重要也是最困難的一步是對(duì)車牌信息的分割,分割的準(zhǔn)確度將直接影響到識(shí)別的結(jié)果。圖像中一些干擾因素如灰塵、雨水、不適當(dāng)?shù)恼彰�、霧和昏暗的光線條件等,將會(huì)使識(shí)別工作更加困難。在車牌圖像識(shí)別車牌中,車牌分割法是從車牌中提取重要數(shù)據(jù)的一種方法。汽車牌照?qǐng)D像的自動(dòng)識(shí)別是汽車牌照應(yīng)用領(lǐng)域面臨的挑戰(zhàn),主要難點(diǎn)在于顏色、字體、尺寸遮擋、位置和不同車牌種類等因素。本文的研究工作主要集中在對(duì)中國(guó)汽車牌照的檢測(cè)、分割和識(shí)別。AVLPSR的主要過程分為三個(gè)步驟:車牌檢測(cè)、字符分割和字符識(shí)別。本文提出了一種簡(jiǎn)單的車牌檢測(cè)算法利用局部二值模式直方圖（LBPH）... 

【文章來源】：北京郵電大學(xué)北京市 211工程院校 教育部直屬院校

【文章頁(yè)數(shù)】：81 頁(yè)

【學(xué)位級(jí)別】：碩士

【文章目錄】：
Abstract
摘要
List of Abbreviations
Chapter 1 Introduction
    1.1 Introduction
    1.2 System Components and Working
    1.3 Detection Background and Techniques
        1.3.1 Object Detection History
        1.3.2 Artificial Neural Network
        1.3.3 Convolutional Neural network algorithm
        1.3.4 Region Based Convolutional Neural Network
        1.3.5 Fast Region Based Convolutional Neural Network
        1.3.6 Faster Region Based Convolutional Neural
    1.4 Segmentation Background and Techniques
        1.4.1 K-means Clustering Algorithm
        1.4.2 Otsu Threshold Algorithm
        1.4.3 Global Thresh holding Algorithms
            1.4.3.1 Histogram based
            1.4.3.2 Clustering based
            1.4.3.3 Entropy based
            1.4.3.4 Gaussian Distributions
            1.4.3.5 Feature Extraction
        1.4.4 K nearest neighbors
    1.5 Recognition Background and Techniques
        1.5.1 Soft Computing Techniques
        1.5.2 Fuzzy Logic
        1.5.3 Template Matching Algorithm
        1.5.4 Structural/Syntactic Algorithm
    1.6 Motivation
    1.7 Summary
Chapter 2 Literature Review
    2.1 Introduction
    2.2 Related Research
    2.3 Literature Survey
    2.4 Problem Statement
Chapter 3 Research Methods of Segmentation and Recognition for Vehicle License PlateDetection
    3.1 Introduction
    3.2 Vehicle License Plate Detection
        3.2.1 Local Binary Pattern Algorithm used for Vehicle License Plate Detection
        3.2.2 Introduction
    3.3 Vehicle License Plate Segmentation
        3.3.1 Connected components algroithm
        3.3.2 Algorithm 1:Information Extracting
        3.3.3 Algorithm 2:Select primary and secondary components
        3.3.4 Algorithm 3:Assign secondary to primary components and output characters of Number Plates
    3.4 Vehicle License Plate Recognition
        3.4.1 Support Vector Machine
    3.5 Summary
Chapter 4 Results and Performance Analysis
    4.1 Software Used
        4.1.1 NetBeans
        4.1.2 Java
        4.1.3 MATLAB
        4.1.4 Jet Brain PyCharm:
        4.1.5 Python
    4.2 Vehicle License Plate Detection
    4.3 Vehicle License Plate Segmentation
    4.4 Vehicle License Plate Recognition
    4.5 Summary
Chapter 5 Conclusion and future work
    5.1 Conclusion
    5.2 Scope and Future Work
References
Acknowledgement
List of Publications



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