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研究生:陳建宏
研究生(外文):Chien-Hung Chen
論文名稱:以車牌辨識技術檢驗機車定檢狀況之研究
論文名稱(外文):The Study of Checking the Annual Inspection Status of Motorcycles Based on License Plate Recognition
指導教授:黃有評黃有評引用關係謝尚琳謝尚琳引用關係
指導教授(外文):Yo-Ping HuangShang-Lin Hsieh
學位類別:碩士
校院名稱:大同大學
系所名稱:資訊工程學系(所)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:92
中文關鍵詞:水平和垂直投影倒傳遞網路模型字元恢復搜尋框特徵比對法車牌辨識
外文關鍵詞:feature matchingsearch windowhorizontal and vertical projectionscharacter recoveryLicense plate recognitionback propagation neural network
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近年來車牌辨識技術已被廣泛應用在贓車協尋、停車場的管控和交通流量資訊等領域。本論文將提出在路邊和定檢站等複雜環境下,以車牌辨識技術設計智慧型機車排氣檢定系統來確認機車是否接受排氣檢定。
我們的平台將同時使用UMPC (Ultra Mobile Personal Computer)加裝網路照相機和桌上型PC來進行實驗。本論文中,車牌辨識系統可分為三個主要子系統:車牌定位、車牌字元切割和字元辨識。車牌定位是利用水平垂直投影整合搜尋框來定位車牌,並使用了字元恢復法和車牌區域過濾法來提高定位成功率,最後進行傾斜車牌的校正;車牌字元切割則是將定位出來的車牌依據車牌和字元的特性,加以分析處理並分割出車牌字元。此外車牌的類型也可以在此程序中被定義;在字元辨識上,將同時採用倒傳遞神經網路和特徵比對法來辨識字元,最後再將辨識結果與資料庫做比對以確認機車是否已定檢。在實驗部份,我們實驗了來自不同定檢站和路邊的車牌影像,其辨識率高達95.7%與93.9%以上。辨識所需時間在UMPC (Celeron 900 MHz, 256MB memory) 上約小於1秒,在個人電腦 (Intel Pentium 4 3.0GHz, 1GB memory) 上只需要約293微秒。論文中將詳細提出路邊定檢與機車車牌辨識所會遭遇到的困難和解決方法。
License plate recognition techniques have been successfully applied to the management of stolen cars, management of parking lots and traffic flow control. This study proposes a license plate based strategy for checking the annual inspection status of motorcycles from images taken along the roadside and at designated inspection stations.
Both a UMPC (Ultra Mobile Personal Computer) with a web camera and a desktop PC are used as the hardware platforms. In this study, the license plate recognition strategy consists of three main parts, including license plate location, segmentation of characters and characters recognition. The license plate locations in images are identified by means of integrated horizontal and vertical projections that are scanned using a search window. Moreover, a character recovery method and a plate-region filter are exploited to enhance the success rate and the tilt license plate will be adjusted. The segmentation of characters uses the feature of license plate of characters to segment each one. Besides, the type of license plates can also be defined in this procedure. Character recognition is achieved using both a back-propagation artificial neural network and feature matching. The identified license plate can then be compared with entries in a database to check the inspection status of the motorcycle. Experiments yield a recognition rate of 95.7% and 93.9% based on test images from roadside and inspection stations, respectively. It takes less than 1 second on a UMPC (Celeron 900MHz with 256MB memory) and about 293 milliseconds on a PC (Intel Pentium 4 3.0GHz with 1GB memory) to correctly recognize a license plate. Challenges associated with recognizing license plates from roadside and designated inspection stations images are also discussed.
ACKNOWLEDGMENTS iii
ENGLISH ABSTRACT iv
CHINESE ABSTRACT v
TABLE OF CONTENTS vi
LIST OF FIGURES ix
LIST OF TABLES xiii
CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Objective 2
1.3 Research Restriction 3
1.4 Thesis Organization 6
CHAPTER 2 RELATED WORK 7
2.1 License Plate Types 7
2.2 System Overview 9
2.3 Background Knowledge 10
2.3.1 Edge Detection 10
2.3.2 Binarization and Threshold Decision 11
2.3.3 Noise Reduction 12
2.3.4 Bilinear Interpolation 12
2.3.5 Thinning Method 14
2.3.6 Neural Network 17
2.4 Related Research 19
2.4.1 License Plate Location 20
2.4.2 Character Segmentation 22
2.4.3 Character Recognition 24
CHAPTER 3 SYSTEM IMPLEMENTATION 28
3.1 The Flowchart of System Architecture 28
3.2 Image Preprocessing 30
3.2.1 Image Capture and Color Space 30
3.2.2 Binarization 30
3.2.3 Reducing Noises by Median Filter 33
3.3 The License Plates Location Module 34
3.3.1 Edge Detection by Using Sobel Operator 34
3.3.2 The Location Method 36
3.3.3 Tilt Estimation and Adjustment 42
3.4 The Segmentation Module 45
3.4.1 Smoothing Image 45
3.4.2 Dilation 45
3.4.3 Removing Unwanted Region 46
3.4.4 The Segmentation Method 49
3.4.5 The Analysis of Character Blocks 53
3.4.6 Characters Recovery 54
3.4.7 Type Definition of Motorcycles 55
3.5 The Recognition Module 57
3.5.1 Normalizing and Binarizng Character Image 57
3.5.2 The Hierarchical Recognition by Using Back Propagation Neural Network 57
3.5.3 The Hierarchical Recognition by Feature Matching Method 63
CHAPTER 4 EXPERIMENTAL RESULTS AND DISCUSSION 65
4.1 Experimental Environment 65
4.2 Experiment of License Plates Location 69
4.3 Experiment of License Plates Recognition 72
4.3.1 Experiment Results of BPNN 72
4.3.2 The Hierarchical Recognition 77
4.4 Evaluation and Analysis 82
4.5 Discussions 84
CHAPTER 5 CONCLUSIONS AND FUTURE WORK 85
5.1 Conclusions 85
5.2 Future Work 87
REFERENCES 88
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