臺灣博碩士論文加值系統

車牌偵測是車牌辨識系統中最關鍵的步驟，如果車牌偵測的定位結果不夠精準，那後續的字元辨識步驟也不用執行了。尤其是在移動式或手持式的車牌辨識裝置上，車牌偵測準確度很容易受到複雜背景、亮度變化及場景變動等外在環境的干擾，因此大部分的傳統車牌辨識系統都只能應用在固定且特定的場景環境中。
為了解決移動式或手持式車牌辨識裝置所遭遇的車牌偵測難題，本論文提出了以AdaBoost車牌偵測方法為主，垂直邊緣(HL2 Vertical Edge)車牌偵測方法為輔的車牌偵測演算法，其中AdaBoost車牌偵測方法又分為Plate-Based AdaBoost車牌偵測方法與Character-Based AdaBoost車牌偵測方法，前者為偵測出可能是車牌的零至多個候選區域，再以後者偵測出的車牌字元進行驗證，便可從這些候選區域中過濾出正確的車牌定位結果。如果前者未偵測出候選區域或後者的驗證結果未輸出車牌，則輔以垂直邊緣車牌偵測方法再偵測一次。從實驗結果可以發現，本論文所提出的車牌偵測方法在複雜背景、亮度變化及場景變動的狀況下的車牌影像具有很高且穩定的定位準確率。
最後，本論文將提出的車牌偵測方法與車牌字元辨識系統整合，並且將其實作成具備即時動態車牌辨識功能的行車記錄器。整體車牌辨識系統的車牌偵測準確率與字元辨識準確率分別為99%與98%。

License Plate Detection (LPD) is the fundamental step to License Plate Recognition (LPR). If the license plate localization result of LPD is not exact enough, subsequent OCR step is unnecessary to be carried out. Most especially, on mobile or portable LPR devices, the accuracy of LPD severely suffers from the environmental interferences, like complex background, illumination variation, scene alternation. So most traditional LPR system only can work out for invariant and specific scenes.
In order to overcome the LPD difficulty of mobile or portable LPR devices, this paper proposes plate- and character-based AdaBoost LPD with vertical-edge-based auxiliary LPD. The proposed plate-based AdaBoost LPD is to localize 0 or more license plate candidates firstly, and then, the proposed character-based AdaBoost LPD is to verify and filter out the most accurate license plate candidate. As for the vertical-edge-based auxiliary LPD, it is activated to double check the scene only after no license plate candidate is detected by the proposed plate- and character-based AdaBoost LPD. From experimental results, it is found that the proposed LPD method can achieve excellent and stable accuracy under various conditions of background complexity, illumination, and scene.
Finally, this paper implements the proposed LPD method and license plate character recognition module into a vehicle video recorder featuring real-time and continuous LPR. The LPD rate and LPR rate of the implementation are 99% and 98%, respectively.

摘 要 i
ABSTRACT iii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix
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 車牌辨識流程 4
1.4 論文架構 6
第二章 車牌偵測方法的相關研究 7
2.1 AdaBoost結合Haar-like特徵的車牌偵測方法 7
2.2 基於傳統Vertical Sobel車牌偵測方法 9
2.3 基於二階二維Haar離散小波轉換的車牌偵測方法 12
2.4 基於Vertical Sobel with AdaBoost的車牌偵測方法 16
第三章 基於車牌與字元訓練樣本的AdaBoost車牌偵測方法 18
第四章 實驗分析 26
4.1 實驗架構 26
4.2 實驗步驟 28
4.3 實驗結果 29
第五章 Android車牌辨識之行車記錄器實作 37
5.1 平台選擇因素 39
5.2 Android手持式系統平台的軟體架構 41
5.3 實作流程與技術說明 46
5.3.1 實用功能描述 46
5.3.2 系統使用技術 48
5.3.3 系統架構與程式流程設計 49
5.3.4 應用的開放原始碼軟體專案 50
5.4 具備即時動態車牌辨識功能的行車記錄器實作成果 52
第六章 結論與未來展望 56
參考文獻 58

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