簡易檢索 / 詳目顯示

研究生: 謝亦信
Sie, Yi-Sin
論文名稱: 以深度學習架構實現之適用於複雜道路環境之車牌辨識系統
A Novel License Plate Recognition System for Complex Road Environment with Deep Learning
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: 53
中文關鍵詞: 車牌辨識系統深度學習卷積類神經網路智慧城市
英文關鍵詞: License plate recognition system, deep learning, Convolutional Neural Networks, smart city
DOI URL: http://doi.org/10.6345/NTNU201900464
論文種類: 學術論文
相關次數: 點閱:171下載:13
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 因應智慧城市的發展,車牌辨識系統的發展對竊盜車輛調查、犯罪車輛追蹤和車流監控等車輛管理的需求迫切重要。例如,為了追蹤被盜或犯罪車輛,必須調閱特定範圍內所有路口監視器畫面,以人力檢視監視畫面以找尋特定車輛與車牌,相當耗時費工,而透過應用於路口監視器的車牌辨識系統,可以快速自動搜尋車輛車牌,建構車輛行進路線,大量減少人力負擔,對犯罪偵防將有重大助益。

    近年來車牌辨識技術已經非常成熟的應用於智慧停車場、交通收費系統等場域,然而運用於路口監視器影像,會面臨諸多挑戰,包括車牌在畫面比例過小、光源不穩定、拍攝角度和車輛移動造成車牌字元模糊、複雜的道路環境、廣告車牌、交通號誌、路名指標等問題。

    傳統車牌辨識系統方法分為三個步驟,包括偵測車牌、車牌字元切割以及字元辨識。本論文提出一個基於深度學習架構的階層式車牌辨識系統,首先在畫面中偵測車牌並將車牌影像擷取下來,接著針對車牌影像執行字元辨識,透過兩階段的方法,增加字元在畫面的比例,進而提高字元辨識準確率,實驗結果顯示,車牌偵測率為98.14%,字元辨識率為97.37%,系統執行速度為23.81 fps。另外,我們使用AOLP資料集測試,測試結果顯示,我們提出的方法相較於其他方法,在車牌偵測以及字元辨識皆有較高的車牌偵測率以及字元辨識率。

    Due to the need to detect and track stolen and criminal vehicles and traffic monitoring, the development of license plate recognition systems on intersection monitors system is very urgent for the development of smart cities. Unlike the traditional license plate recognition technology applied to smart parking lots to identify a single license plate in a single lane, license plate recognition applied to intersection monitors must detect multiple license plates on multiple lanes. In addition, license plate recognition applied to intersection monitors faces many challenges, including too small license plates in the picture, unstable light sources, different shooting angles, blurred license plate characters in moving vehicles, and complex road conditions, advertising signs, traffic signs and road name indicator.

    To solve the above problems, this paper proposes a new two-stage methodology based on deep learning technology which first detects all the license plates in a picture and extracts the license plate images, and then performs character recognition on the license plate images using Convolutional Neural Networks. Through the two-stage approach, this method increases the proportion of characters in the picture, which in turn improves the character recognition accuracy. Experimental results show that the methodology achieves 98.14% of license plate detection rate and 97.37% of character recognition rate. The performance of the hierarchical methodology is about 23.81 fps.

    In addition, we use the AOLP dataset to test. The testing results show that the proposed method has higher license plate detection rate and character recognition rate in license plate detection and character recognition than other methods.

    目錄 v 圖目錄 vii 表目錄 x 第一章 緒論 1 1.1 研究動機與背景 1 1.2 研究目的 5 1.3 研究方法概述 5 1.4 研究貢獻 8 1.5 論文架構 8 第二章 文獻探討 10 2.1 基於CNN方法之車牌辨識系統 10 2.2 基於物件偵測方法之車牌辨識系統 15 2.2.1 R-CNN 16 2.2.2 Fast R-CNN 17 2.2.3 Faster R-CNN 18 2.2.4 SSD 20 2.2.5 YOLO 22 2.2.6 YOLOv2 23 第三章 研究方法 26 3.1 系統流程 26 3.2 偵測車牌 27 3.2.1 輸入影像 28 3.2.2 調整影像尺寸 29 3.2.3 偵測車牌模組 29 3.2.4 偵測車牌結果 30 3.2.5 擷取車牌影像 31 3.3 字元辨識 32 3.3.1 輸入車牌影像 33 3.3.2 調整影像尺寸 33 3.3.3 辨識字元模組 34 3.3.4 字元辨識結果 34 第四章 實驗結果 35 4.1 偵測車牌實驗 36 4.1.1 訓練方法 36 4.1.2 動態影像偵測 37 4.2 字元辨識實驗 40 4.2.1 訓練資料 41 4.2.2 測試 43 4.3 不同架構搭配 46 4.4 AOLP 47 4.5 實驗結論 48 第五章 結論與未來展望 50 5.1 結論 50 5.2 未來展望 50 參 考 文 獻 51

    [1] 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.
    [2] Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
    [3] 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.
    [4] T. Ying, L. Xin and L. Wanxiang, "License plate detection and localization in complex scenes based on deep learning," 2018 Chinese Control And Decision Conference (CCDC), Shenyang, 2018, pp. 6569-6574.
    [5] Q. Wang, “License plate recognition via convolutional neural networks,” 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, 2017, pp. 926-929.
    [6] M. Z. Abedin, A. C. Nath, P. Dhar, K. Deb and M. S. Hossain, "License plate recognition system based on contour properties and deep learning model," 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, 2017, pp. 590-593.
    [7] C. Zhao, Y. Hao, S. Sui and S. Sui, "A New Method to Detect the License Plate in Dynamic Scene," 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), Enshi, 2018, pp. 414-419.
    [8] S. Lee, K. Son, B. Yoon and J. Park, "Video Based License Plate Recognition of Moving Vehicles Using Convolutional Neural Network," 2018 18th International Conference on Control, Automation and Systems (ICCAS), Daegwallyeong, 2018, pp. 1634-1636.
    [9] Krizhevsky, Alex, Ilya Sutskever and Geoffrey E. Hinton. “ImageNet Classification with Deep Convolutional Neural Networks.” Commun. ACM 60 (2012): 84-90.
    [10] 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.
    [11] S. G. Kim, H. G. Jeon and H. I. Koo, "Deep-learning-based license plate detection method using vehicle region extraction," in Electronics Letters, vol. 53, no. 15, pp. 1034-1036, 20 7 2017.
    [12] R. Girshick, "Fast R-CNN," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1440-1448.
    [13] Z. Huang and L. Hou, "Chinese License Plate Detection Based on Deep Neural Network," 2018 International Conference on Control and Robots (ICCR), Hong Kong, 2018, pp. 84-88.
    [14] 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.
    [15] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, et al., "SSD: Single Shot MultiBox Detector," in European Conference on Computer Vision, 2016, pp. 21-37.
    [16] 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.
    [17] 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.
    [18] H. Li, P. Wang and C. Shen, "Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks," in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 1126-1136, March 2019.

    下載圖示
    QR CODE