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研究生： 林永鑫
Lin, Yong-Sin
論文名稱： 植基於卷積神經網路之高效能車牌辨識系統
An Efficient License Plate Recognition System Using Convolution Neural Network
指導教授： 林政宏
Lin, Cheng-Hung
學位類別： 碩士
Master
系所名稱： 電機工程學系
Department of Electrical Engineering
論文出版年： 2018
畢業學年度： 106
語文別： 中文
論文頁數： 45
中文關鍵詞： 車牌辨識系統 、卷積神經網路 、智慧都市
英文關鍵詞： License plate recognition system, convolution neural network, smart city
DOI URL： http://doi.org/10.6345/THE.NTNU.DEE.010.2018.E08
論文種類： 學術論文
相關次數： 點閱：207  下載：49 
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    • 近年來，車牌辨識系統已成為智能城市車輛管理、被盜車輛調查、交通監控等發展中的關鍵角色，車牌辨識系統有三個階段，包括車牌偵測、字元分割，與字元辨識。儘管車牌辨識系統已成功的應用於環境單純的智能停車場，但使用於監控系統中仍會面臨許多問題，例如多車道辨識，大量的交通號誌與廣告招牌，惡劣天氣與夜間拍攝的模糊傾斜圖像。本論文提出了一種高效的車牌辨識系統，首先偵測車輛，再從車輛中偵測車牌，以減少車牌偵測的誤報。再使用卷積神經網路來改善模糊圖像與近似字元的辨識效果，實驗結果顯示，與傳統的車牌辨識系統相比，該系統擁有較高的精確度。

    In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Up to now the license plate recognition system has been successfully applied to the environment-controlled smart parking system, however it still raises many challenges in the surveillance system such as congested traffic with multiple plates, ambiguous signs and advertisements, tilting plates, as well as obscure images that are captured during bad weather and poor light conditions. In this thesis, we propose an efficient license plate recognition system that first detects vehicles and then retrieves license plates from the detected vehicles to reduce false positives on plate detection. Thereafter, the technique of convolution neural networks is applied to improve the character recognition accuracy from the blurred and obscure images. The experimental results show the superiority of the performance in the proposed method as compared to the traditional license plate recognition systems.

    中文摘要 i 英文摘要 ii 誌謝 iv 目錄 v 圖目錄 viii 表目錄 x 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 研究方法概述 4 1.4 研究貢獻 6 1.5 論文架構 6 第二章 文獻探討 9 2.1 車牌偵測 9 2.1.1 依區域邊緣特徵 9 2.1.2 依影像顏色特徵 10 2.1.3 依字元排列特徵 10 2.2 字元切割 12 2.2.1 基於投影 12 2.2.2 基於像素連接 12 2.3 字元辨識 13 2.3.1 字元像素值辨識 13 2.3.2 字元特徵辨識 14 2.4 物件偵測 14 2.4.1 Region-based CNN (R-CNN) 14 2.4.2 Fast Region-based CNN (Fast R-CNN) 15 2.4.3 Faster Region-based CNN (Faster R-CNN) 16 2.4.4 You Only Look Once (YOLO) 17 2.4.5 YOLOv2 18 第三章 研究方法 21 3.1 系統流程 21 3.2 車輛偵測模組 22 3.3 車牌偵測模組 22 3.3.1 訓練階段 22 3.3.2 偵測階段 23 3.4 字元分割 24 3.4.1 二值化 25 3.4.2 投影裁切 26 3.5 字元辨識 27 第四章 實驗結果 29 4.1 車輛偵測實驗 29 4.1.1 訓練YOLOv2 29 4.1.2 動態影像偵測 30 4.2 車牌偵測實驗 31 4.2.1 訓練SVM分類器 32 4.2.2 基於SVM之方法偵測車牌 32 4.2.3 使用YOLOv2偵測車輛再結合SVM偵測車牌 33 4.3 字元分割實驗 34 4.3.1 二值化 34 4.3.2 字元分割 35 4.4 字元辨識實驗 35 4.4.1 訓練 36 4.4.2 測試 37 4.5 實驗結論 38 第五章 結論與未來展望 39 5.1 結論 39 5.2 未來展望 39 參考文獻 40 自傳 45

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