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研究生:羅智群
研究生(外文):Zhi-Chun Luo
論文名稱:基於樣板比對之車牌自動辨識系統
論文名稱(外文):Automatic Vehicle License Plate Recognition System Based on Template Matching
指導教授:劉正忠劉正忠引用關係
指導教授(外文):Chen-Chung Liu
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:電子工程系
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:2011
畢業學年度:99
語文別:中文
論文頁數:88
中文關鍵詞:車牌辨識樣板比對
外文關鍵詞:Vehicle License Plate RecognitionTemplate Matching
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為實現智慧交通系統之理想,自動車牌辨識系統絕對是關鍵的一環,然而自動車牌辨識系統包涵了車牌定位、車牌字元切割和車牌字元辨識三大部份。此三項功能必須依序進行,完成定位後方能進行字元的切割,將車牌上的各個字元,切割成數張的獨立字元影像,方能進行之後的辨識。車牌定位之目的為在影像中,尋找可能的車牌位置,本論文提出之車牌定位演算法主要的步驟有色彩空間轉換、適應性背景更新、去除雜訊、框取車輛、截取輪廓、邊緣細化、刪除連續邊、框取車牌以及調整車牌框架大小等步驟;而字元切割則是對車牌定位後所得的影像進行切割,將車牌中的每個字元獨立出來,本篇論文利用了霍夫轉換法,來找出車牌邊緣的直線,利用此條直線的斜率來進行變形車牌的矯正,並且針對車牌影像計算邊緣梯度圖,利用此圖來進行車牌範圍的收縮,使之範圍收縮至僅剩字元的部份,取得僅剩的字元區塊後,即可利用投影法來進行字元的切割。取得個別字元的影像後,就是進行最後的字元辨識,本篇論文採用樣版比對的方法來進行辨識,並加以改進,字元資料庫共有175張影像,分別為10個阿拉伯數字及25個英文字母 (字母“O”除外) 各5張影像,且皆已正規化大小為 20*40 pixel2。樣版比對時為計算兩張圖的差異點,然後再根據差異點的多寡,給予對應的分數,每五張相同數字或字母的分數會進行加總,因此總共會有35個分數。此分數最高者即為判定之字元。分數計算完成後,則找出評分最高的一組,作為此張輸入字元影像的辨識結果,本論文僅使用了極低的樣本數,但最後的辨識率亦可達九成五以上。降低了樣本數在程式設計的觀點上,不僅僅只是降低了記憶體的耗用,亦直接的減少了執行的迴圈數,增加了執行的效能,表示著我們可以使用更少的資源,更快的速率,達成極高的辨識效果。
Nowadays, vehicle license plate (VLP) recognition system has become a key to lots of traffic related applications, such as road traffic monitoring, traffic analysis, and parking lots access control, etc. Accurately detecting the VLP from a vehicle image, extracting the VLP number from the detected VLPs, and recognizing the extracted VLP number are the main stages of vehicle license plate recognition (VLPR) system. They greatly control the overall recognition accuracy and processing speed of the whole system. The main aim of this research is to find a high effective VLP recognition system. The functions of this system consists of locating the multiple VLPs of moving vehicles from a video traffic image sequence, extracting VLP number from detected VLP, and recognizing the extracted VLP number. Main steps in the VLP locating stage are color space transform, adaptive background updating, noise removal, vehicle frame extraction, edge detecting and thinning, long edge deleting, VLP segmentation, and VLP normalization steps. In the VLP number segmentation stage for an extracted VLP, the proposed algorithm adopts the Sobel operator to obtain the edge map of the VLP, uses Hough transform to obtain the longest straight line segment as the horizontal reference line to calibrate the pose of the extracted VLP, utilizes horizontal boundaries and vertical boundaries detections to trim the VLP region, and employs projection scheme to extract the characters in trimmed VLP region. In the character recognition stage, the VLP character image database is first constructed with 175 normalized character images (10 Arabic numbers and 25 English characters (A ~ Z, except character “O”), and 5 images for each character). The extracted character is normalized into a standard size and is then compared to each element of the character image database. Each comparison will obtain its score according the difference between the extracted character and a database element so that a character has 5 scores. The 5 scores of each character are then summed. The extracted VLP character is identified as the character whose score sum is the maximum of all sore sums and is larger than the threshold defined empirically. The experiment results show that the presented algorithm can (i) correctly localize the VLPs even in overlapped vehicles situation, (ii) effectively extract the VLP number even from a distorted VLP caused by the shifting of relative position between the vehicle and the camera, and (iii) accurately identify the extracted VLP numbers.
中文摘要 III
ABSTRACT V
致謝 VII
目錄 IX
圖目錄 XI
表目錄 XIV
第一章 緒論 1
1.1. 研究背景與動機 1
1.2. 研究目的 2
1.3. 論文系統架構 3
第二章 文獻探討 4
2.1. 車牌定位 4
2.2. 車牌字元切割 6
2.3. 車牌字元辨識 7
第三章 相關原理介紹 9
3.1. 常用色彩空間 9
3.1.1. RGB 10
3.1.2. HSI 11
3.1.3. YIQ 14
3.2. 邊緣偵測 14
3.3. 霍夫轉換 15
第四章 車牌辨識系統 16
4.1. 車牌定位 16
4.1.1. RGB轉HSI色彩空間 17
4.1.2. 適應性背景更新 18
4.1.3. 框取車輛 21
4.1.4. 輪廓擷取 23
4.1.5. 輪廓細線化 25
4.1.6. 刪除連續邊 29
4.1.7. 框取車牌 30
4.1.8. 調整車牌框架大小 31
4.2. 字元切割 32
4.2.1. 色彩像素值調整 33
4.2.2. 車牌傾斜校正 34
4.2.3. 範圍收縮 38
4.2.4. 字元切割 41
4.3. 車牌字元辨識 48
第五章 實驗結果 53
第六章 結論與未來展望 63
參考文獻 65
作者簡介 73

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