Skip to main content
SpringerLink
Log in

A Real-Time License Plate Detection Method Using a Deep Learning Approach

  • Conference paper
  • First Online:
Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

Abstract

In vision-driven Intelligent Transportation Systems (ITS) where cameras play a vital role, accurate detection and re-identification of vehicles are fundamental demands. Hence, recent approaches have employed a wide range of algorithms to provide the best possible accuracy. These methods commonly generate a vehicle detection model based on its visual appearance features such as license-plate, headlights or some other distinguishable specifications. Among different object detection approaches, Deep Neural Networks (DNNs) have the advantage of magnificent detection accuracy in case a huge amount of training data is provided. In this paper, a robust approach for license-plate detection based on YOLO v.3 is proposed which takes advantage of high detection accuracy and real-time performance. The mentioned approach can detect the license-plate location of vehicles as a general representation of vehicle presence in images. To train the model, a dataset of vehicle images with Iranian license-plates has been generated by the authors and augmented to provide a wider range of data for test and train purposes. It should be mentioned that the proposed method can detect the license-plate area as an indicator of vehicle presence with no Optical Character Recognition (OCR) algorithm to distinguish characters inside the license-plate. Experimental results have shown the high performance of the system with precision 0.979 and recall 0.972.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhu, F., Li, Z., Chen, S., Xiong, G.: Parallel Transportation Management and Control System and Its Applications in Building Smart Cities. IEEE Trans. Intell. Transp. Syst. 17, 1576–1585 (2016). https://doi.org/10.1109/TITS.2015.2506156

    Article  Google Scholar 

  2. Zhang, J., Wang, F.Y., Wang, K., et al.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12, 1624–1639 (2011). https://doi.org/10.1109/TITS.2011.2158001

    Article  Google Scholar 

  3. Bommes, M., Fazekas, A., Volkenhoff, T., Oeser, M.: Video based intelligent transportation systems - state of the art and future development. Transp. Res. Procedia 14, 4495–4504 (2016). https://doi.org/10.1016/j.trpro.2016.05.372

    Article  Google Scholar 

  4. Tian, B., Yao, Q., Gu, Y., et al.: Video processing techniques for traffic flow monitoring: a survey. IEEE Conference on Intelligent Transportation Systems Proceedings, ITSC, pp. 1103–1108 (2011). https://doi.org/10.1109/ITSC.2011.6083125

  5. O’Mahony, N., et al.: Deep learning vs. traditional computer vision. In: Arai, K., Kapoor, S. (eds.) CVC 2019. AISC, vol. 943, pp. 128–144. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17795-9_10

    Chapter  Google Scholar 

  6. Pouyanfar, S., Sadiq, S., Yan, Y., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 51 (2018). https://doi.org/10.1145/3234150

  7. Deng, L.: A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inf. Process. 3 (2014). https://doi.org/10.1017/atsip.2013.9

  8. Hatt, M., Parmar, C., Qi, J., El Naqa, I.: Machine (deep) learning methods for image processing and radiomics. IEEE Trans. Radiat. Plasma Med. Sci. 3, 104–108 (2019). https://doi.org/10.1109/trpms.2019.2899538

    Article  Google Scholar 

  9. Zeiler, M.D., Fergus, R.: Visualizing and Understanding Convolutional Neural Networks, pp. 1–9 (2012)

    Google Scholar 

  10. Aloysius, N., Geetha, M.: A review on deep convolutional neural networks. In: Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing ICCSP 2017, January 2018, pp. 588–592 (2018). https://doi.org/10.1109/ICCSP.2017.8286426

  11. Uijlings, J.R.R., Van De Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vis. 104, 154–171 (2013). https://doi.org/10.1007/s11263-013-0620-5

    Article  Google Scholar 

  12. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014). https://doi.org/10.1109/CVPR.2014.81

  13. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  14. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  15. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 2016, pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91

  16. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, January 2017, pp. 6517–6525 (2017). https://doi.org/10.1109/CVPR.2017.690

  17. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018). https://arxiv.org/abs/1804.02767

  18. Shin, J.S., Kim, U.T., Lee, D.K., et al.: Real-time vehicle detection using deep learning scheme on embedded system. In: International Conference on Ubiquitous and Future Networks, ICUFN, Milan, pp 272–274 (2017)

    Google Scholar 

  19. Huval, B., Wang, T., Tandon, S., et al.: An empirical evaluation of deep learning on highway driving (2015). http://arxiv.org/abs/1504.01716

  20. Wang, J.G., Zhou, L., Pan, Y., et al.: Appearance-based brake-lights recognition using deep learning and vehicle detection. In: IEEE Intelligent Vehicles Symposium Proceedings, August 2016, pp. 815–820 (2016). https://doi.org/10.1109/IVS.2016.7535481

  21. Hsu, S.C., Huang, C.L., Chuang, C.H.: Vehicle detection using simplified fast R-CNN. In: 2018 International Workshop on Advanced Image Technology, IWAIT 2018, pp. 1–3 (2018). https://doi.org/10.1109/IWAIT.2018.8369767

  22. Zhang, Q., Wan, C., Han, W.: A modified faster region-based convolutional neural network approach for improved vehicle detection performance. Multimedia Tools Appl. 78(20), 29431–29446 (2018). https://doi.org/10.1007/s11042-018-6769-8

    Article  Google Scholar 

  23. Wang, L., Liao, J., Xu, C.: Vehicle detection based on drone images with the improved faster R-CNN. In: ACM International Conference Proceeding Series Part F 148150, pp. 466–471 (2019). https://doi.org/10.1145/3318299.3318383

  24. Kim, S.G., Jeon, H.G., Koo, H.I.: Deep-learning-based license plate detection method using vehicle region extraction. Electron. Lett. 53, 1034–1036 (2017). https://doi.org/10.1049/el.2017.1373

    Article  Google Scholar 

  25. Selmi, Z., Ben Halima, M., Alimi, A.M.: Deep learning system for automatic license plate detection and recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 1, pp. 1132–1138 (2018). https://doi.org/10.1109/ICDAR.2017.187

  26. Silva, S.M., Jung, C.R.: Real-time Brazilian license plate detection and recognition using deep convolutional neural networks. In: Proceedings of the 30th Conference on Graphics, Patterns and Images, SIBGRAPI 2017, pp. 55–62 (2017). https://doi.org/10.1109/SIBGRAPI.2017.14

  27. Abdullah, S., Mahedi Hasan, M., Muhammad Saiful Islam, S.: YOLO-based three-stage network for Bangla license plate recognition in Dhaka metropolitan city. In: 2018 International Conference on Bangla Speech and Language Processing ICBSLP (2018). https://doi.org/10.1109/ICBSLP.2018.8554668

  28. Puarungroj, W., Boonsirisumpun, N.: Thai license plate recognition based on deep learning. Procedia Comput. Sci. 135, 214–221 (2018). https://doi.org/10.1016/j.procs.2018.08.168

    Article  Google Scholar 

  29. Tourani, A., Soroori, S., Shahbahrami, A., et al.: A robust vehicle detection approach based on faster R-CNN algorithm. In: 4th International Conference on Pattern Recognition and Image Analysis IPRIA 2019, pp. 119–123 (2019). https://doi.org/10.1109/PRIA.2019.8785988

  30. Dutta, A., Zisserman, A.: The VIA annotation software for images, audio and video. In: MM 2019 – Proceedings of the 27th ACM International Conference on Multimedia, pp. 2276–2279 (2019). https://doi.org/10.1145/3343031.3350535

Download references

Author information

Authors and Affiliations

  1. Centre for Pattern Recognition and Machine Intelligence, Concordia University Montreal, Montreal, Canada

    Saeed Khazaee & Ching Y. Suen

  2. Department of Computer Engineering, University of Guilan, Rasht, Iran

    Ali Tourani, Sajjad Soroori & Asadollah Shahbahrami

Authors
  1. Saeed Khazaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Tourani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sajjad Soroori
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Asadollah Shahbahrami
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ching Y. Suen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeed Khazaee .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khazaee, S., Tourani, A., Soroori, S., Shahbahrami, A., Suen, C.Y. (2020). A Real-Time License Plate Detection Method Using a Deep Learning Approach. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59830-3_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation