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研究生： 李穎
Li, Ying
論文名稱： 基於Mask R-CNN之傾斜角度車牌識別系統
A License Plate Recognition System for Severe Tilt Angles Using Mask R-CNN
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： 2019
畢業學年度： 107
語文別： 中文
論文頁數： 39
中文關鍵詞： 車牌辨識系統 、深度學習 、Mask R-CNN
英文關鍵詞： license plate recognition systems, deep learning, Mask R-CNN
DOI URL： http://doi.org/10.6345/NTNU201900729
論文種類： 學術論文
相關次數： 點閱：172  下載：0 
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    • 在過去幾年中，車牌辨識系統已經廣泛用於停車場。為了容易識別車牌，停車場中使用的傳統車牌識別系統具有固定的光源和拍攝角度。對於特別傾斜的角度，例如使用超廣角鏡頭或魚眼鏡頭拍攝的車牌影像，車牌特徵變形可能特別嚴重，以致於使用傳統車牌辨識系統的辨識效果不良。本論文中，我們提出了一種基於Mask R-CNN的三階段車牌辨識系統，可用於各種拍攝角度和更傾斜的影像。實驗結果表明，該架構可識別水平傾斜角度超過0~60度的車牌，mAP可達91％。與使用YOLOv2模型的方法相比，本論文提出使用Mask R-CNN的方法在辨識傾斜45度以上的字元方面取得了重大進展。

    In the past few years, license plate recognition systems have been widely used in parking lots. In order to identify license plates easily, traditional license plate recognition systems used in the parking lot have a fixed light source and a shooting angle. For particularly tilting angles, such as license plate images taken with super wide-angle lenses or fisheye lenses, the deformation of the license plate can be particularly severe, resulting in poor recognition of traditional license plate recognition systems. In this paper, we propose a three-stage license plate recognition system based on Mask R-CNN that can be used for various shooting angles and more oblique images. Experimental results show that the proposed architecture can identify license plates with bevel angles over 0~60 degrees and achieve mAP rates of up to 91%. Compared with the approach using YOLOv2 model, the proposed method with Mask R-CNN has made significant progress in identifying characters that are inclined above 45 degrees.

    摘要 i ABSTRACT ii 誌　　謝 iii 目　　錄 iv 圖　目　錄 vi 表　目　錄 vii 第一章 緒論 1 1.1 研究動機與背景 1 1.2 研究目的 3 第二章 文獻探討 5 2.1 歷年車牌辨識方式 5 2.2 深度學習與電腦視覺類型 6 2.2.1 CNN 7 2.2.2 R-CNN[8] 8 2.2.3 SPPnet[9] 8 2.2.4 Fast R-CNN[10] 9 2.2.5 Faster R-CNN[3] 10 2.2.6 YOLO[7] 12 2.2.7 YOLOv2[6] 12 2.2.8 SSD[11] 13 2.2.9 FPN[4] 13 2.2.10 FCN[5] 16 2.2.11 Mask R-CNN[1] 18 第三章 研究方法 21 3.1 三階段車牌辨識系統架構 21 3.1.1 系統流程 21 3.1.2 車輛檢測 24 3.1.3 車牌抓取 24 3.1.4 字元辨識 24 3.2 影像大小調配 25 3.3 分類 25 3.4 標記 25 第四章 實驗結果 27 4.1 實驗設備 27 4.2 實驗環境 27 4.3 訓練與驗證資料集 27 4.4 多角度實驗資料集 28 4.5 AOLP資料集[22] 31 4.6 實驗結果與分析 31 第五章 結論與未來展望 35 5.1 結論 35 5.2 未來展望 35 參　考　文　獻 36 自　　傳 39

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