International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Detection and Recognition of Vehicle License Plates using Deep Learning in Video Surveillance

  • Farooq, Muhammad Umer (Department of Computer Science and Information Technology, NED University of Engineering and Technology) ;
  • Ahmed, Saad (Department of Computer Science, IQRA University Karachi Pakistan) ;
  • Latif, Mustafa (Department of Software Engineering, NED University of Engineering and Technology) ;
  • Jawaid, Danish (Department of Computer Science and Information Technology, NED University of Engineering and Technology) ;
  • Khan, Muhammad Zofeen (Department of Computer Science and Information Technology, NED University of Engineering and Technology) ;
  • Khan, Yahya (Department of Computer Science and Information Technology, NED University of Engineering and Technology)
  • Received : 2022.11.05
  • Published : 2022.11.30
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Abstract

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

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References

  1. Farooq MU, Khan NA, Ali MS. Unsupervised video surveillance for anomaly detection of street traffic. International Journal of Advanced Computer Science and Applications. 2017;8(12).
  2. Farooq MU, Ahmed A, Khan SM, Nawaz MB. Estimation of Traffic Occupancy using Image Segmentation. Engineering, Technology & Applied Science Research. 2021 Aug 21;11(4):7291-5. https://doi.org/10.48084/etasr.4218
  3. Kocer HE, Cevik KK. Artificial neural networks based vehicle license plate recognition. Procedia Computer Science. 2011 Jan 1;3:1033-7. https://doi.org/10.1016/j.procs.2010.12.169
  4. Sahu, Chinmaya Kumar, Sushree Barsa Pattnayak, Susantini Behera, and Manas Ranjan Mohanty. "A Comparative Analysis of Deep Learning Approach for Automatic Number Plate Recognition." In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 932-937. IEEE, 2020.
  5. Laroca, Rayson, Evair Severo, Luiz A. Zanlorensi, Luiz S. Oliveira, Gabriel Resende Goncalves, William Robson Schwartz, and David Menotti. "A robust real-time automatic license plate recognition based on the YOLO detector." In 2018 international joint conference on neural networks (ijcnn), pp. 1-10. IEEE, 2018.
  6. Lin, Cheng-Hung, Yong-Sin Lin, and Wei-Chen Liu. "An efficient license plate recognition system using convolution neural networks." In 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 224-227. IEEE, 2018.
  7. Chen, Rung-Ching. "Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning." Image and Vision Computing 87 (2019): 47-56. https://doi.org/10.1016/j.imavis.2019.04.007
  8. Qadri, Muhammad Tahir, and Muhammad Asif. "Automatic number plate recognition system for vehicle identification using optical character recognition." In 2009 International Conference on Education Technology and Computer, pp. 335-338. IEEE, 2009.
  9. Patel, Chirag, Dipti Shah, and Atul Patel. "Automatic number plate recognition system (anpr): A survey." International Journal of Computer Applications 69, no. 9 (2013).
  10. Gnanaprakash, V., N. Kanthimathi, and N. Saranya. "Automatic number plate recognition using deep learning." In IOP Conference Series: Materials Science and Engineering, vol. 1084, no. 1, p. 012027. IOP Publishing, 2021.
  11. Saeed, Maham, Muhammad Gufran Khan, Adil Zulfiqar, and Muhammad Tahir Bhatti. "Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR." Complexity 2021 (2021).
  12. Caner, H., Gecim, H.S. and Alkar, A.Z., 2008. Efficient embedded neural-network-based license plate recognition system. IEEE Transactions on Vehicular Technology, 57(5), pp.2675-2683. https://doi.org/10.1109/TVT.2008.915524
  13. Zou, Yongjie, Yongjun Zhang, Jun Yan, Xiaoxu Jiang, Tengjie Huang, Haisheng Fan, and Zhongwei Cui. "License plate detection and recognition based on YOLOv3 and ILPRNET." Signal, Image and Video Processing 16, no. 2 (2022): 473-480. https://doi.org/10.1007/s11760-021-01981-8
  14. Jamtsho, Yonten, Panomkhawn Riyamongkol, and Rattapoom Waranusast. "Real-time Bhutanese license plate localization using YOLO." ICT Express 6, no. 2 (2020): 121-124. https://doi.org/10.1016/j.icte.2019.11.001
  15. Ahn, Hyochang, and Han-Jin Cho. "Research of automatic recognition of car license plates based on deep learning for convergence traffic control system." Personal and Ubiquitous Computing (2021): 1-10.
  16. Park, Se-Ho, Saet-Byeol Yu, Jeong-Ah Kim, and Hyoseok Yoon. "An all-in-one vehicle type and license plate recognition system using YOLOv4." Sensors 22, no. 3 (2022): 921. https://doi.org/10.3390/s22030921
  17. Beratoglu MS, Toreyіn BU. Vehicle license plate detector in compressed domain. IEEE Access. 2021 Jun 28;9:95087-96. https://doi.org/10.1109/ACCESS.2021.3092938