SpringerLink
Log in

Multi-models Based on Yolov8 for Identification of Vehicle Type and License Plate Recognition

  • Conference paper
  • First Online:
New Trends in Information and Communications Technology Applications (NTICT 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2096))

  • 14 Accesses

Abstract

Embedded systems with cameras and deep learning techniques have been shown to be flexible and good at finding different targets in the areas of intelligent monitoring and urban mobility. These use cases are present in diverse situations and regions. The collection of pertinent data from the deployment site is of utmost importance. This study introduces an innovative methodology for a comprehensive system that integrates vehicle category identification with license plate recognition using the YOLOv8 algorithm. The system comprises three main components: vehicle type detection and recognition, detection of the license plate, and detection of the license plate characters and numbers. The suggested approach intends to enhance the identification system’s applicability in the unique context of Iraqi vehicles, particularly on roadways and in cities and their environments. The dataset used in this study was obtained from various areas inside Iraq. The detection system employed in our research successfully identified three distinct vehicle classes as well as detected and recognized license plates in both Arabic and English. The mean average precision achieved for the aforementioned tasks was 97.5%, 98.94%, 98.6%, and 98.4%, respectively. Through the use of visual data, such as images and videos, our system successfully identified license plates with reduced dimensions. It is posited that our technology has the potential to be used in densely populated areas in order to cater to the substantial requirements for improved visual acuity in smart urban environments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 94.15
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 90.94
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gharaibeh, A., Salahuddin, M.A., Hussini, S.J., Khreishah, A., Khalil, I., Guizani, M., Al-Fuqaha, A.: Smart cities: A survey on data management, security, and enabling technologies. IEEE Communications Surveys & Tutorials 19(4), 2456–2501 (2017)

    Article  Google Scholar 

  2. Winkler, T., Rinner, B.: Security and privacy protection in visual sensor networks: A survey. ACM Computing Surveys (CSUR) 47(1), 1–42 (2014)

    Article  Google Scholar 

  3. Won, M.: Intelligent traffic monitoring systems for vehicle classification: A survey. IEEE Access 8, 73340–73358 (2020)

    Article  Google Scholar 

  4. Baran, R., Rusc, T., Fornalski, P.: A smart camera for the surveillance of vehicles in intelligent transportation systems. Multimedia Tools and Applications 75, 10471–10493 (2016)

    Article  Google Scholar 

  5. Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikäinen, M.: Deep learning for generic object detection: A survey. Int. J. Comput. Vision 128, 261–318 (2020)

    Article  Google Scholar 

  6. Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (alpr): A state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 23(2), 311–325 (2012)

    Article  Google Scholar 

  7. Anagnostopoulos, C.-N.E., Anagnostopoulos, I.E., Psoroulas, I.D., Loumos, V., Kayafas, E.: License plate recognition from still images and video sequences: A survey. IEEE Trans. Intell. Transp. Syst. 9(3), 377–391 (2008)

    Article  Google Scholar 

  8. Shashirangana, J., Padmasiri, H., Meedeniya, D., Perera, C.: Automated license plate recognition: a survey on methods and techniques. IEEE Access 9, 11203–11225 (2020)

    Article  Google Scholar 

  9. Siddiqui, A.J., Mammeri, A., Boukerche, A.: Real-time vehicle make and model recognition based on a bag of surf features. IEEE Trans. Intell. Transp. Syst. 17(11), 3205–3219 (2016)

    Article  Google Scholar 

  10. Manzoor, M.A., Morgan, Y., Bais, A.: Real-time vehicle make and model recognition system. Machine Learning and Knowledge Extraction 1(2), 611–629 (2019)

    Article  Google Scholar 

  11. Hsieh, J.-W., Chen, L.-C., Chen, D.-Y.: Symmetrical surf and its applications to vehicle detection and vehicle make and model recognition. IEEE Trans. Intell. Transp. Syst. 15(1), 6–20 (2014)

    Article  Google Scholar 

  12. G. Jocher, A. Chaurasia, and J. Qiu, “Yolo by ultralytics,” URL: https://githubcom/ultralytics/ultralytics, 2023

    Google Scholar 

  13. K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision, pp. 2961–2969, 2017

    Google Scholar 

  14. R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision, pp. 1440–1448, 2015

    Google Scholar 

  15. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37, Springer, 2016

    Google Scholar 

  16. J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” ar**v preprint ar**v:1804.02767, 2018

  17. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” ar**v preprint ar**v:2004.10934, 2020

  18. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788, 2016

    Google Scholar 

  19. J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263–7271, 2017

    Google Scholar 

  20. Naaman, O.: Resnet and lstm based accurate approach for license plate detection and recognition. Traitement du Signal 39(5), 1577 (2022)

    Article  Google Scholar 

  21. D. Habeeb, F. Noman, A. A. Alkahtani, Y. A. Alsariera, G. Alkawsi, Y. Fazea, A. M. Al-Jubari, et al., “Deep-learning-based approach for iraqi and malaysian vehicle license plate recognition,” Computational intelligence and neuroscience, vol. 2021, 2021

    Google Scholar 

  22. Omar, N., Sengur, A., Al-Ali, S.G.S.: Cascaded deep learning-based efficient approach for license plate detection and recognition. Expert Syst. Appl. 149, 113280 (2020)

    Article  Google Scholar 

  23. D. A. Abd Alhamza and A. D. Alaythawy, “Iraqi license plate recognition based on machine learning,” Iraqi Journal of Information and Communication Technology, vol. 3, no. 4, pp. 1–10, 2020

    Google Scholar 

  24. Abbass, G.Y., Marhoon, A.F.: Iraqi license plate detection and segmentation based on deep learning. Iraqi Journal for Electrical and Electronic Engineering 17(2), 102–107 (2021)

    Article  Google Scholar 

  25. S. T. Ahmed, D. A. Hammood, R. F. Chisab, A. Al-Naji, and J. Chahl, “Medical image encryption: A comprehensive review,” Computers, vol. 12, no. 8, 2023

    Google Scholar 

  26. R. S. Jebur, C. S. Der, and D. A. Hammood, “A review and taxonomy of image denoising techniques,” in 2020 6th International Conference on Interactive Digital Media (ICIDM), pp. 1–6, IEEE, 2020

    Google Scholar 

  27. Willson, R.G., Shafer, S.A.: What is the center of the image? JOSA A 11(11), 2946–2955 (1994)

    Article  Google Scholar 

  28. A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka, “3d bounding box estimation using deep learning and geometry,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 7074–7082, 2017

    Google Scholar 

  29. Wang, L., Zhang, Y., Feng, J.: On the euclidean distance of images. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1334–1339 (2005)

    Article  Google Scholar 

  30. Roboflow, “Roboflow: Your machine learning data pipeline,” Year of Access. Accessed on Date of Access

    Google Scholar 

  31. E. Bisong and E. Bisong, “Google colaboratory,” Building machine learning and deep learning models on google cloud platform: a comprehensive guide for beginners, pp. 59–64, 2019

    Google Scholar 

  32. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al., “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems, vol. 32, 2019

    Google Scholar 

  33. S. Imambi, K. B. Prakash, and G. Kanagachidambaresan, “Pytorch,” Programming with TensorFlow: Solution for Edge Computing Applications, pp. 87–104, 2021

    Google Scholar 

  34. P. Henderson and V. Ferrari, “End-to-end training of object class detectors for mean average precision,” in Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part V 13, pp. 198–213, Springer, 2017

    Google Scholar 

  35. **e, L., Ahmad, T., **, L., Liu, Y., Zhang, S.: A new cnn-based method for multi-directional car license plate detection. IEEE Trans. Intell. Transp. Syst. 19(2), 507–517 (2018)

    Article  Google Scholar 

  36. Li, H., Wang, P., You, M., Shen, C.: Reading car license plates using deep neural networks. Image Vis. Comput. 72, 14–23 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Technical College, Department of Computer Technical Engineering, Middle Technical University (MTU), Al Doura, 10022, Baghdad, Iraq

    Mustafa Noaman Kadhim, Ammar Hussein Mutlag & Dalal Abdulmohsin Hammood

Authors
  1. Mustafa Noaman Kadhim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ammar Hussein Mutlag
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dalal Abdulmohsin Hammood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dalal Abdulmohsin Hammood .

Editor information

Editors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Abbas M. Al-Bakry

  2. University of Information Technology and Communications, Baghdad, Iraq

    Mouayad A. Sahib

  3. University of Babylon, Babylon, Iraq

    Safaa O. Al-Mamory

  4. University of Information Technology and Communications, Baghdad, Iraq

    Jaafar A. Aldhaibani

  5. University of Information Technology and Communications, Baghdad, Iraq

    Ali N. Al-Shuwaili

  6. University of Information Technology and Communications, Baghdad, Iraq

    Haitham S. Hasan

  7. University of Information Technology and Communications, Baghdad, Iraq

    Rula A. Hamid

  8. University of Babylon, Babylon, Iraq

    Ali K. Idrees

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kadhim, M.N., Mutlag, A.H., Hammood, D.A. (2024). Multi-models Based on Yolov8 for Identification of Vehicle Type and License Plate Recognition. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2023. Communications in Computer and Information Science, vol 2096. Springer, Cham. https://doi.org/10.1007/978-3-031-62814-6_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-62814-6_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-62813-9

  • Online ISBN: 978-3-031-62814-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation