臺灣博碩士論文加值系統

License Plate Recognition (LPR) has recently played a critical role in applications of Intelligent Transportation System (ITS), such as parking spaces management, records of traffic violations, and searches for stolen cars.
However, although research on license plate recognition has been undertaken over many years, in practical applications, the accuracy of detection and identification can decrease because of the complex backgrounds and light changes in capture image.
Therefore, recognition systems should be limited to specific environmental settings to obtain favorable results. In summary, interference caused by the environment is the main factor that limits the development of LPR.
LPR and License Plate Detection (LPD) comprise two main steps, character segmentation and character identification. The license plate zone is detected prior to the two steps, and is the key to successful recognition of the license plate zone.
Most LPR studies have investigated static and low-resolution images. Conversely, the present study involved using 720P high-definition video and a PCIe (PCI Express) image capture card to detect license plates. Subsequently, to achieve real-time LPR, Haar Wavelet Transform was applied in extracting an HL image, reducing the algorithm calculation time.
Chapter 1 provides a review of relevant literature, and introduces the system architecture developed in this study.
Chapter 2 introduces related techniques, including color space transform, motion detection, image sharpening, histogram equalization, Haar wavelet transform, edge detection, binarization, and connected component labeling.
Chapter 3 describes the algorithm used for LPD in detail. First, the motion object is detected. The license plate features are then intensified, and adaptive thresholding is conducted to binarize the image. The edge is then connected, and connected component labeling is used to label each block. Thus, the license plate location is obtained.
Chapter 4 introduces the experimental equipment and software interface used. In addition the experimental results obtained using the aforementioned algorithm in this study are described.
In the final chapter, a conclusion is presented and possible methods of the amendments are suggested in accordance with the experimental results.

摘要 I
ABSTRACT III
目錄 V
圖目錄 VII
表目錄 X
第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 3
1.3 系統架構 5
1.4 論文架構 5
第二章 影像處理相關技術 6
2.1 色彩空間 6
2.2 移動偵測 10
2.3 影像銳化 11
2.4 直方圖等化 12
2.5 Haar Discrete Wavelet Transform 15
2.6 二值化 19
2.7 邊緣偵測 25
2.8 連通物件標記法 27
2.9 水平投影與垂直投影 30
第三章 車牌偵測演算法 32
3.1 演算法流程 32
3.2 演算法步驟 34
3.2.1 影像擷取與色彩空間轉換 34
3.2.2 移動物體偵測 35
3.2.3 影像銳化 39
3.2.4 直方圖等化 41
3.2.5 Haar 離散小波轉換與二值化 43
3.2.6 邊緣偵測 48
3.2.7 邊緣點連接 50
3.2.8 群組編號與消除雜訊區塊 51
3.2.9 篩選車牌條件 52
3.2.10 自訂參數 59
第四章 實驗結果 61
4.1 實驗設備介紹 61
4.2 軟體介面 64
4.3 即時偵測系統測試 65
第五章 結論與未來展望 70
5.1 結論 70
5.2 未來展望 71
REFERENCES 72

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