Skip to main content
SpringerLink
Log in

Vehicle license plate detection using region-based convolutional neural networks

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Vehicle license plate (LP) detection is a relatively complex problem until we assume the use of a static camera, variations in illumination, known templates of the LP, guaranteed color patterns and other simple assumptions. Practical applications demand robust and generalized LP detection techniques to accommodate complex scenarios. This work suggests a new approach to solving this problem by treating the vehicle LP as an object. The primary focus of this study is to address following tasks associated with the challenge of LP detection: (1) LP detection in every frame of a video sequence, (2) detection of partial LPs and (3) detection of LPs with moving cameras and moving vehicles. The state-of-the-art object detection techniques, including convolutional neural networks with region proposal (RCNN), its successors (Fast-RCNN and Faster-RCNN) and the exemplar-SVM, are used in this work to provide solutions to the problem. The suggested study demonstrates better results in comprehensive tests and comparisons than other conventional approaches.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://www.medialab.ntua.gr/research/LPRdatabase.html.

  2. http://mlv.gist.ac.kr.

References

  • Al-Ghaili AM, Mashohor S, Ramli AR, Ismail A (2013) Vertical-edge-based car-license-plate detection method. IEEE Trans Veh Technol 62:26–38

    Article  Google Scholar 

  • Anagnostopoulos C, Anagnostopoulos I, Loumos V, Kayafas E (2006) A license plate-recognition algorithm for intelligent transportation system applications. IEEE Trans Intell Transp Syst 7(3):377–392

    Article  Google Scholar 

  • Anagnostopoulos CNE, Anagnostopoulos IE, Psoroulas ID, Loumos V, Kayafas E (2008) License plate recognition from still images and video sequences: a survey. IEEE Trans Intell Transp Syst 9(3):377–391

    Article  Google Scholar 

  • Ashtari AH, Nordin MJ, Fathy M (2014) An iranian license plate recognition system based on color features. IEEE Trans Intell Transp Syst 15:1690–1705

    Article  Google Scholar 

  • Broitman M, Klopovsky Y, Silinskis N (2013) License plate detection algorithm, vol 9067, p 90670Z

  • Chavali N, Agrawal H, Mahendru A, Batra D (2015) Object-proposal evaluation protocol is ’gameable’. In: CoRR, vol abs/1505.05836

  • Du S, Ibrahim M, Shehata M, Badawy W (2013) Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans Circuits Syst Video Technol 23(2):311–325

    Article  Google Scholar 

  • Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2007) The pascal visual object classes challenge 2007 (voc2007) results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html

  • Girshick R (2015) Fast R-CNN. In: Proceedings of the international conference on computer vision (ICCV)

  • Girshick R, Donahue J, Darrell T, Malik J (2016) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38:142–158

    Article  Google Scholar 

  • Gu Q, Yang J, Kong L, Cui G (2015) Multi-scaled license plate detection based on the label-moveable maximal MSER clique. Opt Rev 22(4):669–678

    Article  Google Scholar 

  • Hosang J, Benenson R, Dollr P, Schiele B (2016) What makes for effective detection proposals? IEEE Trans Pattern Anal Mach Intell 38:814–830

    Article  Google Scholar 

  • Hsu GS, Chen JC, Chung YZ (2013) Application-oriented license plate recognition. IEEE Trans Veh Technol 62:552–561

    Article  Google Scholar 

  • Ilonen J, Kamarainen J-K, Paalanen P, Hamouz M, Kittler J, Kaviainen H (2008) Image feature localization by multiple hypothesis testing of Gabor features. IEEE Trans Image Process 17:311–325

    Article  MathSciNet  Google Scholar 

  • Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. arXiv:1408.5093

  • Kobayashi T (2015) Three viewpoints toward exemplar svm. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 2765–2773

  • Lalimi MA, Ghofrani S, McLernon D (2012) A vehicle license plate detection method using region and edge based methods. Comput Electr Eng 39(3):834–845

    Article  Google Scholar 

  • Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  • Li Q (2014) A geometric framework for rectangular shape detection. IEEE Trans Image Process 23:4139–4149

    Article  MathSciNet  MATH  Google Scholar 

  • Malisiewicz T (2011) Exemplar-based representations for object detection, association and beyond. PhD thesis, The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania, vol 15213, p 7

  • Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. doi:10.1109/TPAMI.2016.2577031

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  • Smara GA, Khalefah F (2014) Localization of license plate number using dynamic image processing techniques and genetic algorithms. IEEE Trans Evol Comput 18:244–257

    Article  Google Scholar 

  • Tadic V, Popovic M, Odry P (2016) Fuzzified Gabor filter for license plate detection. Eng Appl Artif Intell 48:40–58

    Article  Google Scholar 

  • Uijlings JRR, Sande KEA, Gevers T, Smeulders AWM (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171

    Article  Google Scholar 

  • Wang R, Sang N, Wang R, Jiang L (2014a) Detection and tracking strategy for license plate detection in video. Opt Int J Light Electron Opt 125(10):2283–2288

    Article  Google Scholar 

  • Wang R, Sang N, Huang R, Wang Y (2014b) License plate detection using gradient information and cascade detectors. Opt Int J Light Electron Opt 125(1):186–190

    Article  Google Scholar 

  • Xing X, Choi B-J, Chae S, Lee M-H (2009) Design of a recognizing system for vehicle’s license plates with English characters. Int J Fuzzy Logic Intell Syst 9(3):166–171

    Article  Google Scholar 

  • Yao Z, Yi W (2014) License plate detection based on multistage information fusion. Inf Fusion 18(1):78–85

    Article  Google Scholar 

  • Zeiler M, Fergus R (2014) Visualizing and understanding convolutional networks. In: Computer VisionECCV 2014, vol 8689, pp 818–833

  • Zheng L, He X, Samali B, Yang LT (2013) An algorithm for accuracy enhancement of license plate recognition. J Comput Syst Sci 79(2):245–255

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the Institute for Information and Communications Technology Promotion (IITP) Grant funded by the Korea government (MSIP) (No. B0101-15-0525, Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis).

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea

    Muhammad Aasim Rafique & Moongu Jeon

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Witold Pedrycz

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

    Witold Pedrycz

Authors
  1. Muhammad Aasim Rafique
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Witold Pedrycz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Moongu Jeon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Moongu Jeon.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rafique, M.A., Pedrycz, W. & Jeon, M. Vehicle license plate detection using region-based convolutional neural networks. Soft Comput 22, 6429–6440 (2018). https://doi.org/10.1007/s00500-017-2696-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2696-2

Keywords

Search

Navigation