Proposing Smart System Using Super Vector Machine and WPOD-NET Models for Detecting Car Number Plate

Phat Huu Nguyen

Abstract


Nowadays, there are many smart parking lots using plate detection system, which is set in the entrances to control in/out vehicle. It is noticeable that those systems have drawbacks such as: working in a fixed environment, necessity of manual labor and requirement of checkpoints set in entrances. To overcome those problems, this paper proposes a car numbers detection system using a cutting-edge technology of machine learning. A Convolutional Neural Network (CNN)-Warped Planar Object Detection (WPOD-NET)-is used to detect the car plate and a modified support vector machine (SVM) model for Vietnam car registration number is applied as an optical character recognition (OCR). Comparing to other models, the proposed one improves not only the range of detection angle but also the accuracy of detecting in shady conditions. The proportion of experimental result is 95.1 percent (detecting correctly full plate) when testing 1000 samples in various reality scenarios.

Keywords


Convolutional neural network; Support vector machine; Car Number Plate; LSTM; automated systems

References


M. Srgio and J. Claudio, “License plate detection and recognition in unconstrained scenarios,” in Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision ECCV 2018, vol. 11216, 09 2018, pp. 593–609.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision (ECCV), 2016, pp. 1–15.

J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” CoRR, vol. abs/1804.02767, 2018. [Online]. Available: http://arxiv.org/ abs/1804.02767

M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” 2015.

S. Montazzolli and C. Jung, “Real-time brazilian license plate detection and recognition using deep convolutional neural networks,” in 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2017, pp. 55–62.

T. Duan, D. Tran, P. Tran, and N. Hoang, “Building an automatic vehicle license-plate recognition system,” in International. Conference in Computer Science RIVF05, 02 2005, pp. 59–63.

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 Trans- portation Systems, vol. 7, no. 3, pp. 377–392, 2006.

Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, and Sei-Wan Chen, “Automatic license plate recognition,” IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 1, pp. 42–53, 2004.

K. Roy, A. M. Shabbir Khan, M. Zariff Ahsham Ali, S. R. Simanto, N. Mohammed, M. A. Atick, S. Islam, and K. M. Islam, “An analytical approach for enhancing the automatic detection and recognition of skewed bangla license plates,” in International Conference on Bangla Speech and Language Processing (ICBSLP), 2019, pp. 1–4.

D. Berchmans and S. S. Kumar, “Optical character recognition: An overview and an insight,” in 2014 International Conference on Con- trol, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014, pp. 1361–1365.

R. Smith, “An overview of the tesseract ocr engine,” in Ninth Inter- national Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, 2007, pp. 629–633.

C. Nello and S.-T. John, “An introduction to support vector machines and other kernel-based learning methods. repr,” Introduction to Support Vector Machines and other Kernel-Based Learning Methods, vol. 22, 01 2001.

L. Zheng, X. He, Q. Wu, and T. Hintz, “Number plate recognition without segmentation,” pp. 164–168, 01 2007.

T. C. Mota and A. C. G. Thome, “One-against-all-based multiclass svm strategies applied to vehicle plate character recognition,” in 2009 International Joint Conference on Neural Networks, 2009, pp. 2153– 2159.

V. Tran-Quang, P. N. Huu, and T. Miyoshi, “Adaptive transmission range assignment algorithm for in-routing image compression on wireless sensor networks,” in Proceedings of 3rd Intl Conf. Commun. Electron. (ICCE 2010), Aug. 2010, pp. 1–6.

P. N. Huu, V. Tran-Quang, and T. Miyoshi, “Low-complexity and energy-efficient algorithms on image compression for wireless sensor networks,” IEICE Transactions on Communications, vol. E93-B, no. 12, pp. 3438–3447, Dec. 2010.

V. Tran-Quang, P. N. Huu, and T. Miyoshi, “A transmission range opti- mization algorithm to avoid energy holes in wireless sensor networks,” IEICE Trans. Commun., vol. E94-B, no. 11, pp. 3026–3036, Nov. 2011.

C. T. Phan, D. D. Pham, H. V. Tran, and P. N. Huu, “Applying the iot platform and green wave theory to control intelligent traffic lights system for urban areas in vietnam,” KSII Trans. on Internet and Infor. Syst., vol. 13, no. 1, pp. 34–51, Jan. 2019.

T. N. Duong, V. D. Than, T. H. Tran, Q. H. Dang, D. M. Nguyen, and H. M. Pham, “An effective similarity measure for neighborhood- based collaborative filtering,” in 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 2018, pp. 250–254.

V. Tran-Quang, P. N. Huu, and T. Miyoshi, “A collaborative target tracking algorithm considering energy constraint inwsns,” in 19th Intl Conf. Software, Telecomm. Comput. Netw. (SoftCOM 2011), Sept. 2011, pp. 1–5.

P. N. Huu, T. Tran Van, and N. G. Thi, “Proposing distortion compensa- tion algorithm for determining distance using two cameras,” in 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), 2019, pp. 172–177.

P. N. Huu, V. Tran-Quang, and T. Miyoshi, “Energy threshold adaptation algorithms on image compression to prolong wsn lifetime,” in 2010 7th International Symposium on Wireless Communication Systems, 2010, pp. 834–838.

Hendry, R.-C. Chen, “Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning,” Image and Vision Computing, vol. 87, pp. 47-56, 2019.




DOI: http://doi.org/10.11591/ijai.v10.i3.pp%25p

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