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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅楸善(Chiou-Shann Fuh) | |
| dc.contributor.author | Chi-Hsuan Huang | en |
| dc.contributor.author | 黃季軒 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:32:54Z | - |
| dc.date.copyright | 2020-07-17 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-06 | |
| dc.identifier.citation | [1] A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, https://arxiv.org/pdf/2004.10934.pdf, 2020. [2] G. R. Gonçalves, S. P. G. da Silva, D. Menotti, and W. R. Schwartz, “Benchmark for License Plate Character Segmentation,” Journal of Electronic Imaging, Vol. 25, No. 5, pp. 1–5, 2016. [3] A. Graves, S. Fernandez, F. Gomez, and J. Schmidhuber, “Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks,” Proceedings of International Conference on Machine Learning, Pittsburgh, Pennsylvania, pp. 369-376, 2006. [4] Intel, “Intel Distribution of OpenVINO Toolkit,” https://software.intel.com/en-us/openvino-toolkit, 2020. [5] P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, pp. 1125-1134, 2017. [6] R. Laroca, L. A. Zanlorensi, G. R. Gonçalves, E. Todt, W. R. Schwartz, and D. Menotti, “An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO Detector,” arXiv preprint arXiv:1909.01754, https://arxiv.org/pdf/1909.01754.pdf, 2019. [7] H. Li, P. Wang, and C. Shen, “Toward End-to-End Car License Plate Detection and Recognition with Deep Neural Networks,” IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 3, pp. 1126-1136, 2018. [8] T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection,” Proceedings of IEEE International Conference on Computer Vision, Venice, Italy, pp. 2980-2988, 2017. [9] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single Shot MultiBox Detector,” Proceedings of the European Conference on Computer Vision, Amsterdam, Netherlands, LNCS, Vol. 9905, pp. 21-37, 2016. [10] F. K. Peng, “The Spatial and Temporal Analysis of the Impacts of Video Surveillance System on the Theft Crime Rates,” Master Thesis, Department of Crime Prevention and Corrections, Central Police University, https://hdl.handle.net/11296/4swq89, 2015。 [11] M. M. Rashid, A. Musa, M. A. Rahman, N. Farahana, and A. Farhana, “Automatic Parking Management System and Parking Fee Collection Based on Number Plate Recognition,” International Journal of Machine Learning and Computing, Vol. 2, No. 2, pp. 93-98, 2012. [12] J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, pp. 6517-6525, 2017. [13] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, pp. 4510-4520, 2018. [14] S. M. Silva and C. R. Jung, “Real-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks,” Proceedings of Conference on Graphics, Patterns, and Images (SIBGRAPI), Niterói, Brazil, pp. 55–62, 2017. [15] S. M. Silva and C. R. Jung, “License Plate Detection and Recognition in Unconstrained Scenarios,” Proceedings of the European Conference on Computer Vision, Munich, Germany, LNCS, Vol. 11216, pp. 593-609, 2018. [16] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, https://arxiv.org/pdf/1409.1556.pdf, 2014. [17] S. Zherzdev and A. Gruzdev, “LPRNet: License Plate Recognition via Deep Neural Networks,” arXiv preprint arXiv:1806.10447, https://arxiv.org/pdf/1806.10447.pdf, 2018. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15324 | - |
| dc.description.abstract | 本論文提出一個人工智慧(Artificial Intelligence)解決方案，用於移動載具上進行車牌辨識。於移動中的載具上裝設攝影機，其所拍攝到的車牌影像富有各式不同角度及光影變化，然而傳統電腦視覺演算法無法有效克服於不同的環境下進行車牌辨識。因此，本研究透過深度學習(Deep Learning)方式執行車牌辨識，以因應攝影機所拍攝畫面之光源、角度等環境變因，進而提升辨識準確率。此外，電源供應在移動載具上並非易事，因此電源消耗亦為重點考量，故本研究選擇輕量的網路架構。本系統之辨識流程包含兩個階段──車牌位置偵測及車牌號碼辨識。首先，透過一卷積類神經網路(Convolutional Neural Network, CNN)模型架構執行車牌位置偵測，並將偵測到的車牌影像擷取後進行旋轉校正。接著，設計另一CNN模型架構識別字元，即大寫字母(A-Z)和數字(0-9)。本研究提出之方法於白天高速行駛時可達到95.7%精確率和95%召回率。 | zh_TW |
| dc.description.abstract | In this thesis, an AI (Artificial Intelligence) solution for LPR (License Plate Recognition) on moving vehicles is proposed. The license plates in images captured with cameras on moving vehicles have unpredictable distortion and various illumination which make traditional machine vision algorithms unable to recognize the numbers correctly. Therefore, deep learning is leveraged to recognize license plate in such challenging conditions for better recognition accuracy. Additionally, lightweight neural networks are chosen since the power supply of scooter is quite limited. A two-stage method is presented to recognize license plate. First, the license plates in captured images are detected using CNN (Convolutional Neural Network) model and the rotation of the detected license plates are corrected. Subsequently, the characters are recognized as upper-case format (A-Z) and digits (0-9) with second CNN model. Experimental results show that our system achieves 95.7% precision and 95% recall at high speed during the daytime. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:32:54Z (GMT). No. of bitstreams: 1 U0001-0307202015583100.pdf: 8088679 bytes, checksum: 626b2dbb2a94161800cffc00bb39b04c (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES xiii Chapter 1 Introduction 1 1.1 Overview 1 1.2 License Plates in Natural Scene 5 1.3 Thesis Organization 8 Chapter 2 Related Works 9 Chapter 3 Background 11 3.1 MobileNetV2 11 3.2 SSD: Single-Shot MultiBox Detector 17 3.3 Connectionist Temporal Classification 21 3.3.1 Overview 21 3.3.2 From Network Outputs to Sequences 23 3.3.3 CTC Loss Function 25 3.4 OpenVINO: Open Visual Inference and Neural Network Optimization 26 3.4.1 Overview 26 3.4.2 Deployment Workflow 27 Chapter 4 Methodology 29 4.1 Overview 29 4.2 License Plate Detection 30 4.3 License Plate Recognition 35 4.3.1 Focal CTC Loss 38 4.4 Data Augmentation 40 Chapter 5 Experimental Results 44 5.1 Overview 44 5.2 Datasets 45 5.2.1 Scene Dataset 45 5.2.2 License Plate Dataset 47 5.3 Performance Evaluation Methods 49 5.4 Results and Comparisons 52 5.4.1 Training Stage 52 5.4.2 Testing Stage 55 Chapter 6 Conclusion and Future Works 69 References 71 | |
| dc.language.iso | en | |
| dc.title | 基於深度學習之車牌辨識 | zh_TW |
| dc.title | Vehicle License Plate Recognition with Deep Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 沈裕池(Yu-Chih Shen),賴志宏(Chih-Hung Lai),沈立健(Li-Chien Shen) | |
| dc.subject.keyword | 車牌辨識,深度學習,電腦視覺,人工智慧,卷積類神經網路, | zh_TW |
| dc.subject.keyword | License Plate Recognition,Deep Learning,Computer Vision,Artificial Intelligence,Convolutional Neural Networks, | en |
| dc.relation.page | 74 | |
| dc.identifier.doi | 10.6342/NTU202001297 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-07-07 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| U0001-0307202015583100.pdf 目前未授權公開取用 | 7.9 MB | Adobe PDF |
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