臺灣博碩士論文加值系統

交通運輸對於國家而言是一個相當重要的議題，隨著民眾因為便利性的需求，使得車輛數量日益漸增，在如此龐大的數量之下，所衍伸出來的將是該如何有效處理車輛進出管制、停車收費等問題，自動化車牌辨識能夠使這些問題更有效率的解決。以電腦作為平台所開發的車牌辨識系統，其體積與耗電量大，對於需長時間執行的系統而言相當不利，若將系統移植到嵌入式平台中實現將能改善此缺點，因此本研究選用德州儀器DM6437 EVM作為開發平台，並搭配攝影機與LCD顯示器完成一套即時車牌辨識系統。一般來說，車牌影像的取得是透過固定架設於停車場進出口上方或兩旁的攝影機所取得的，由於車輛停靠時距離攝影機的位置與角度皆不固定，因此所擷取到的車牌影像將有傾斜角度產生。本文使用霍夫轉換作為偵測傾斜角度主要方法，傾斜校正後，透過字元標籤化進行字元分割，將分割後之字元利用階層式字元辨識系統進行結果判斷。為驗證系統之可行性，本論文以停車場之車輛進行測試，結果顯示傾斜校正成功率為88.89%，整體車牌辨識率達81.63%。

Transportation is an important topical subject to a country. Under the public demand for convenience, the number of vehicles has continued to increase, which has led to the problems concerning the effective management of vehicle access control and parking fee collection. The automatic license plate recognition technology can solve these problems efficiently. However, the computer-based license plate recognition system has large volume and is power-consuming, thus, is disadvantageous for long-term operation. If the system can be operated on an embedded platform, the problems can thus be solved. Therefore, this study used Texas Instrument DM6437 EVM as the development platform, as well as cameras and LCD display to complete a real-time license plate recognition system. The license plate images are first captured by the cameras installed over or on the side of the parking garage entrance. As the distance and angle between the parking vehicles and the cameras are variable, the images may have angle of tilts. Hough transform is used to detect the inclination. After tilt correction, the character labeling is performed for character segmentation, and the segmented characters are put into the hierarchical character recognition system to identify the results. In order to validate the system feasibility, this study conducted experiments on the vehicles in the parking garages, and proved that the success rate of tilt correction is 88.89% and the overall recognition rate is 81.63%.

中文摘要i
英文摘要ii
誌謝iii
目錄v
表目錄vii
圖目錄viii
第一章緒論1
1.1研究背景1
1.2研究動機與目的2
1.3文獻回顧3
1.4論文架構5
1.5論文貢獻5
第二章車牌定位6
2.1色彩空間轉換6
2.2直方圖等化7
2.3邊緣檢測9
2.4雜訊濾除10
2.5形態學11
2.5.1膨脹11
2.5.2侵蝕12
2.5.3閉合13
2.6連通成分標示法14
2.7車牌區域判斷15
第三章車牌傾斜校正與辨識16
3.1霍夫轉換16
3.2近鄰旋轉法18
3.3Otsu二值化18
3.4投影法 20
3.5字元影像標籤化與分割21
3.6字元正規化21
3.7階層式字元辨識22
3.7.1樣板比對法23
3.7.2相似字元判斷23
第四章嵌入式系統架構25
4.1Code Composer Studio整合式開發環境25
4.1.1IQmath函式庫27
4.1.2網路開發套件28
4.1.3燒錄至評估板30
4.2數位訊號處理器31
4.2.1TMS320DM6437評估板32
4.2.2增強式直接記憶體存取控制器34
4.2.3 視訊處理子系統35
第五章 實驗結果與討論37
5.1系統架構37
5.1.1演算法流程37
5.1.2硬體架構流程39
5.2環境架設44
5.3車牌定位結果與討論46
5.4車牌傾斜校正結果與討論48
5.5字元分割與辨識結果與討論50
第六章結論與未來展望53
6.1結論53
6.2未來工作與展望53
參考文獻55

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