研究生: |
李志文 Chih-Wen Lee |
---|---|
論文名稱: |
嵌入式即時多標的汽機車牌照辨識系統 Embedded Real-Time Multiple Vehicle License Plate Recognition System |
指導教授: |
許孟超
Mon-Chau Shie |
口試委員: |
梁文耀
Wen-Yau Liang 蔡坤霖 Kun-Lin Tsai 阮聖彰 Shanq-Jang Ruan |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電子工程系 Department of Electronic and Computer Engineering |
論文出版年: | 2008 |
畢業學年度: | 96 |
語文別: | 中文 |
論文頁數: | 69 |
中文關鍵詞: | 車牌辨識 、嵌入式 、汽機車牌照辨識 、即時 、車牌二值化 |
外文關鍵詞: | license plate binarization |
相關次數: | 點閱:319 下載:7 |
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臺灣汽機車密度名列世界前茅,但國內卻缺乏嵌入式汽機車牌照辨識系統之研究,本篇論文提出不需高階攝影機配合即能有效處理交通主要幹道汽機車牌照之即時多標的汽機車牌照辨識系統,解決一般攝影鏡頭動態影像來源之車輛牌照對比不足、髒污、歪斜狀況,並能運作在無DSP輔助之嵌入式系統上。
系統主要分為三個階段,分別為牌照定位、牌照處理與字元辨識,針對來源牌照影像不清之情形提出了「適應牌照之二值化門檻值尋找」方法來處理牌照,與常用二值化方法「區域Otsu」比較,本論文所提之方法於汽車牌照之系統辨識率提升了24.39%,在機車則提升了25.54%,在字元辨識的樣板比對部分利用字元輪廓特徵將樣板字元分為數個子集合,可有效避免6, 9, S之誤判,0, D, 8, B則透過統計左右輪廓外之背景像素比例來作為再次判斷的依據。
系統在交通主要幹道實際測試,汽機車牌照辨識率可達82.92%與80.85%,定位成功率可達96%以上,於嵌入式系統運作的效率在XScale-PXA270 624 Mhz / Linux環境下單一牌照時處理速度約可達到7 ~ 8 fps。
In Taiwan, density of vehicles is come out top in the world. However, it is short of embedded recognition system for license plates of cars and motorcycles in domestic study. In this paper, we proposed a DSP-less embedded system of Real-Time multi-target recognition without professional video camera to solve license plate images in conditions of low-contrast, blur, or skew.
The system is composed of three modules: localization, processing of vehicle plates, and character recognition. For the unclear vehicle plate images, we proposed a method which is suitable to binarize the license plate. Compared with localized Otsu’s method of 3 × 3 block processing, our method achieved 24.39% improvement for cars’ license plates recognition accuracy, and 25.56% improvement for motorcycles’ license plates. To improve template matching, we separated the character templates into some subsets by character’s outline, and could effectively avoid miss-recognition of the characters 6, 9, S. Moreover, by calculating the ratio of left and right side’s background pixels of character’s contour, the characters 0, D, 8, B could be authentically recognized.
The experimental videos were captured upon a sky bridge on Roosevelt Road, Taipei. The accuracies of car’s and motorcycle’s license plate recognition are 82.92% and 80.85%, and the accuracy of localization is 96%. The performance on embedded system (XScale-PXA270 624 MHz / Linux) is 7~8 fps.
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