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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：229  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

    [1] K. He, G. Gkioxari, P. Dollár and R. Girshick, "Mask R-CNN," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 2980-2988.
    [2] K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 770-778.
    [3] S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 1 June 2017.
    [4] T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie, "Feature Pyramid Networks for Object Detection," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 936-944.
    [5] J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3431-3440.
    [6] J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 6517-6525.
    [7] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 779-788.
    [8] R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 580-587.
    [9] K. He, X. Zhang, S. Ren and J. Sun, "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904-1916, 1 Sept. 2015.
    [10] R. Girshick, "Fast R-CNN," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1440-1448.
    [11] W. Liu , D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu and A. Berg, "SSD: Single Shot MultiBox Detector," ECCV , 2016, pp. 1-17.
    [12] M. Yu and Y. Kim. "An Approach to Korean License Plate Recognition Based on Vertical Edge Matching," SMC, 2000 , pp.1-6.
    [13] J. Wang, W. Zhou, J. Xue and X. Liu, "The research and realization of vehicle license plate character segmentation and recognition technology," 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, 2010, pp. 101-104.
    [14] Y. Wen, Y. Lu, J. Yan, Z. Zhou, K. M. von Deneen and P. Shi, "An Algorithm for License Plate Recognition Applied to Intelligent Transportation System, " IEEE Transactions on Intelligent Transportation Systems (T-ITS), vol. 12, no. 3, pp. 830-845, Sept. 2011.
    [15] S. Karasu, A. Altan, Z. Sarac,and R. Hacioglu, "Histogram based vehicle license plate recognition with KNN method," ICAT, Rifat, 2017, pp. 1-4.
    [16] C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos and E. Kayafas, "A License Plate-Recognition Algorithm for Intelligent Transportation System Applications," IEEE Transactions on Intelligent Transportation Systems (T-ITS), vol. 7, no. 3, pp. 377-392, Sept. 2006.
    [17] T. Nukano, M. Fukumi, and M. Khalid, "Vehicle license plate character recognition by neural networks," Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems 2004 (ISPACS 2004), Seoul, South Korea, 2004, pp. 771-775.
    [18] S. Li and Y. Li, "A Recognition Algorithm for Similar Characters on License Plates Based on Improved CNN," 2015 11th International Conference on Computational Intelligence and Security (CIS), Shenzhen, 2015, pp. 1-4.
    [19] C. Lin, Y. Lin and W. Liu, "An efficient license plate recognition system using convolution neural networks," 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, 2018, pp. 224-227.
    [20] C. Lin and Y. Sie, "Two-Stage License Plate Recognition System Using Deep learning," ICASI, 2019, pp. 1-4.
    [21] S. Abdullah, M. Mahedi Hasan and S. Muhammad Saiful Islam, "YOLO-Based Three-Stage Network for Bangla License Plate Recognition in Dhaka Metropolitan City," 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, 2018, pp. 1-6.
    [22] G. Hsu, J. Chen and Y. Chung, "Application-Oriented License Plate Recognition," in IEEE Transactions on Vehicular Technology, vol. 62, no. 2, pp. 552-561, Feb. 2013.
    [23] J. Jiao, Q. Ye, and Q. Huang, “A configurable method for multi style license plate recognition, ”Pattern Recognit., 2009, pp.358–369.
    [24] C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos and E. Kayafas, "A License Plate-Recognition Algorithm for Intelligent Transportation System Applications," in IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 3, pp. 377-392, Sept. 2006.
    [25] Z. Selmi, M. Ben Halima and A. M. Alimi, "Deep Learning System for Automatic License Plate Detection and Recognition," 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, 2017, pp. 1132-1138.

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