Automatic license plate recognition using improved deep learning

معتمد, سارا

doi:10.22052/scj.2024.253175.1163

Automatic license plate recognition using improved deep learning

نوع مقاله : مقاله پژوهشی

نویسنده

سارا معتمد

گروه مهندسی کامپیوتر، واحد فومن و شفت، دانشگاه آزاد اسلامی، فومن، ایران.

10.22052/scj.2024.253175.1163

چکیده

According to today's statistics, more than half a billion vehicles move around the world, making inspection and monitoring one of the basic needs of any traffic control system. All vehicles have an identification number exhibited as the license plate, their primary ID, a vital element. Deep Learning methods are adopted to detect vehicle license plates. This proposed method consists of two steps: highlighting the license plate and reading the ID stages. In this context, the combination of deep neural networks (DNN) and the competitive generative adversarial network (GAN) is applied in the encoding-coder network/structure for this highlighting. The proposed models are assessed based on the FZU Cars and Stanford Cars datasets, to which the results of this study are compared and discussed. The findings here indicate that the accuracy of this proposed model is almost 98%, subject to the two datasets.

کلیدواژه‌ها

موضوعات

هوش مصنوعی

عنوان مقاله [English]

Automatic license plate recognition using improved deep learning

نویسنده [English]

Sara Motamed

Department of Computer Engineering, Fouman and Shaft Branch, Islamic Azad University, Fouman, Iran.

چکیده [English]

کلیدواژه‌ها [English]

License plate recognition
Deep learning
Neural networks
Generative adversarial network
Encoding-decoding structure

مراجع

[1] C.-H. Huang, Y. Sun, and C.-S. Fuh, Technologies to Advance Automation in Forensic Science and Criminal Investigation. IGI Global, Hershey, PA, 2022, ch. Vehicle License Plate Recognition With Deep Learning, pp. 161–219, doi: 10.4018/978-1-7998-8386-9.ch009.
[2] J. Pirgazi, M. M. Pourhashem Kallehbasti, and A. Ghanbari Sorkhi, “An end-to-end deep learning approach for plate recognition in intelligent transportation systems,” Wirel. Commun. Mobile Comput., vol. 2022, no. 1, p. 3364921, 2022, doi: 10.1155/2022/3364921.
[3] W. Wang and T. Jiaoyang, “Research on license plate recognition algorithms based on deep learning in complex environment,” IEEE Access, vol. 8, pp. 91 661–91 675, 2020, doi: 10.1109/ACCESS.2020.2994287.
[4] Y. Zhang, Z. Wang, and J. Zhuang, “Efficient license plate recognition via holistic position attention,” in Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 2021, pp. 3438–3446, doi: 10.1609/AAAI.V35I4.16457.
[5] R. Balia, S. Barra, S. Carta, G. Fenu, A. S. Podda, and N. Sansoni, “A deep learning solution for integrated traffic control through automatic license plate recognition,” in Computational Science and Its Applications - ICCSA 2021 - 21st International Conference, Cagliari, Italy, September 13-16, 2021, Proceedings, Part III, ser. Lecture Notes in Computer Science, O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blecic, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, and C. M. Torre, Eds., vol. 12951. Springer, 2021, pp. 211–226, doi: 10.1007/978-3-030-86970-0_16.
[6] T. Vaiyapuri, S. Mohanty, M. Sivaram, I. Pustokhina, D. Pustokhin, and K. Shankar, “Automatic vehicle license plate recognition using optimal deep learning model,” Comput. Mater. Continua., vol. 67, pp. 1881–1897, 12 2020, doi: 10.32604/cmc.2021.014924.
[7] V. Gnanaprakash, N. Kanthimathi, and N. Saranya, “Automatic number plate recognition using deep learning,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 1084, no. 1, p. 012027, 2021, doi: 10.1088/1757-899X/1084/1/012027.
[8] T. Toroyan, K. Iaych, and M. Peden, “Global status report on road safety,” World Health Organization, Tech. Rep., 2015.
[9] N. N. Kyaw, G. R. Sinha, and K. L. Mon, “License plate recognition of myanmar vehicle number plates A critical review,” in IEEE 7th Global Conference on Consumer Electronics, GCCE 2018, Nara, Japan, October 9-12, 2018. IEEE, 2018, pp. 771–774, doi: 10.1109/GCCE.2018.8574751.
[10] A. Kashyap, B. Suresh, A. Patil, S. Sharma, and A. Jaiswal, “Automatic number plate recognition,” in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018, pp. 838–843, doi: 10.1109/ICACCCN.2018.8748287.
[11] K. Deb, M. I. Khan, M. R. Alam, and K.-H. Jo, “Optical recognition of vehicle license plates,” in Proceedings of 2011 6th International Forum on Strategic Technology, vol. 2, 2011, pp. 743–748, doi: 10.1109/IFOST.2011.6021129.
[12] F. Wang, L. Man, B. Wang, Y. Xiao, W. Pan, and X. Lu, “Fuzzy-based algorithm for color recognition of license plates,” Pattern Recognit. Lett., vol. 29, no. 7, pp. 1007–1020, 2008, doi: 10.1016/J.PATREC.2008.01.026.
[13] S. Yu, B. Li, Q. Zhang, C. Liu, and M. Q. Meng, “A novel license plate location method based on wavelet transform and EMD analysis,” Pattern Recognit., vol. 48, no. 1, pp. 114–125, 2015, doi: 10.1016/J.PATCOG.2014.07.027.
[14] E. Rashedi and H. Nezamabadi-pour, “A hierarchical algorithm for vehicle license plate localization,” Multim. Tools Appl., vol. 77, no. 2, pp. 2771–2790, 2018, doi: 10.1007/S11042-017-4429-Z.
[15] O. Ibitoye, T. Ejidokun, O. Dada, and O. Omitola, “Convolutional neural network-based license plate recognition techniques: A short overview,” in 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 2020, pp. 1529–1532, doi: 10.1109/CSCI51800.2020.00283.
[16] Y. Nakagawa and A. Rosenfeld, “Some experiments on variable thresholding,” Pattern Recognit., vol. 11, no. 3, pp. 191–204, 1979, doi: 10.1016/0031-3203(79)90006-2.
[17] T. Nukano, M. Fukumi, and M. Khalid, “Vehicle license plate character recognition by neural networks,” in Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004., 2004, pp. 771–775, doi: 10.1109/ISPACS.2004.1439164.
[18] I. Giannoukos, C. Anagnostopoulos, V. Loumos, and E. Kayafas, “Operator context scanning to support high segmentation rates for real time license plate recognition,” Pattern Recognit., vol. 43, no. 11, pp. 3866–3878, 2010, doi: 10.1016/J.PATCOG.2010.06.008.
[19] T. Duan, D. Tran, P. Tran, and N. Hoang, “Building an automatic vehicle license-plate recognition system,” in Proc. Int. Conf. Comput. Sci. RIVF, 02 2005.
[20] B. Shan, “Vehicle license plate recognition based on text-line construction and multilevel RBF neural network,” J. Comput., vol. 6, no. 2, pp. 246–253, 2011, doi: 10.4304/JCP.6.2.246-253.
[21] S. Nomura, K. Yamanaka, O. Katai, H. Kawakami, and T. Shiose, “A novel adaptive morphological approach for degraded character image segmentation,” Pattern Recognit., vol. 38, no. 11, pp. 1961–1975, 2005, doi: 10.1016/J.PATCOG.2005.01.026.
[22] A. Kendall, V. Badrinarayanan, and R. Cipolla, “Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding,” in British Machine Vision Conference 2017, BMVC 2017, London, UK, September 4-7, 2017. BMVA Press, 2017.
[23] T. Naito, T. Tsukada, K. Yamada, K. Kozuka, and S. Yamamoto, “Robust license-plate recognition method for passing vehicles under outside environment,” IEEE Trans. Veh. Technol., vol. 49, no. 6, pp. 2309–2319, 2000, doi: 10.1109/25.901900.
[24] M. Kim and Y. Kwon, “Multi-font and multisize character recognition based on the sampling and quantization of an unwrapped contour,” in 13th International Conference on Pattern Recognition, ICPR 1996, Vienna, Austria, 25-19 August, 1996. IEEE Computer Society, 1996, pp. 170–174, doi: 10.1109/ICPR.1996.546816.
[25] P. Hu, Y. Zhao, Z. Yang, and J. Wang, “Recognition of gray character using gabor filters,” in Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997), vol. 1, 2002, pp. 419–424, doi: 10.1109/ICIF.2002.1021184.
[26] M. Ko and Y. Kim, “A simple OCR method from strong perspective view,” in 33rd Applied Image Pattern Recognition Workshop (AIPR 2004), Emerging Technologies and Applications for Imagery Pattern Recognition, 13-15 October 2004, Washington, DC, USA, Proceedings. IEEE Computer Society, 2004, pp. 235–240, doi: 10.1109/AIPR.2004.8.
[27] J. Jiao, Q. Ye, and Q. Huang, “A configurable method for multi-style license plate recognition,” Pattern Recognit., vol. 42, no. 3, pp. 358–369, 2009, doi: 10.1016/J.PATCOG.2008.08.016.
[28] S. Fadaei, A. Kavari, A. Dehghani, and K. Rahimizadeh, “Automatic license plate recognition system based on morphological operations and vertical histogram image processing,” Soft Comput. J., vol. 10, no. 2, pp. 84–97, 2022, doi: 10.22052/scj.2022.243224.1014 [In Persian].
[29] N. Girinath, C. G. Babu, B. Vidhya, S. Agathiyan, P. A. Nandha, S. A. Rigin, and S. A. Pandian, “Automatic number plate detection using deep learning,” in 2022 Smart Technologies, Communication and Robotics (STCR), 2022, pp. 1–5, doi: 10.1109/STCR55312.2022.10009582.
[30] P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008, ser. ACM International Conference Proceeding Series, W. W. Cohen, A. McCallum, and S. T. Roweis, Eds., vol. 307. ACM, 2008, pp. 1096–1103, doi: 10.1145/1390156.1390294.
[31] M.-R. Feizi-Derakhshi, Z. Mottaghinia, and M. Asgari-Chenaghlu, “Persian text classification based on deep neural networks,” Soft Comput. J., vol. 11, no. 1, pp. 120–139, 2022, doi: 10.22052/scj.2023.243182.1010 [In Persian].
[32] S. Najafi and S. Noferesti, “Determining dynamic time quantum in round-robin scheduling algorithm using machine learning,” Soft Comput. J., vol. 10, no. 2, pp. 32–43, 2022, doi: 10.22052/scj.2022.243181.1002 [In Persian].
[33] M. Ebtia, S. M. Hoseini, and R. Khochiani, “Credit rating of bank customers using a new ensemble method based on support vector machine: a case study of pasargad bank,” Soft Comput. J., vol. 10, no. 2, pp. 2–15, 2022, doi: 10.22052/scj.2022.243227.1016 [In Persian].