Skip to main content
SpringerLink
Log in

License plate detection and recognition using hierarchical feature layers from CNN

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In recent years, a variety of systems using deep convolutional neural network (CNN) approaches have achieved good performance on license plate detection and character recognition. However, most of these systems are not stable when the scenes changed, specification of each hierarchical layer to get the final detection result, which can detect multi-scale license plates from an input image. Meanwhile, at the stage of character recognition, data annotation is heavy and time-consuming, giving rise to a large burden on training a better model. We devise an algorithm to generate annotated training data automatically and approximate the data from the real scenes. Our system used for detecting license plate achieves 99.99% mean average precision (mAP) on OpenITS datasets. Character recognition also sees high accuracy, thus verifying the superiority of our method.

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
Fig. 8

Similar content being viewed by others

References

  1. Abolghasemi V, Ahmadyfard A (2009) An edge-based color-aided method for license plate detection. Image Vis Comput 27(8):1134

    Article  Google Scholar 

  2. Alexe B, Deselaers T, Ferrari V (2012) Measuring the Objectness of Image Windows. IEEE Trans Pattern Anal Mach Intell 34(11):2189

    Article  Google Scholar 

  3. Arbelaez P, Ponttuset J, Barron J, Marques F, Malik J (2014) Multiscale Combinatorial Grouping, in Computer Vision and Pattern Recognition, 328–335

  4. Cao G, Chen J, Jiang J (2003) An adaptive approach to vehicle license plate localization, in Industrial Electronics Society, 2003. IECON ‘03. the Conference of the IEEE, 1786–1791 Vol.2

  5. Chen YN, Han CC, Wang CT, Jeng BS (2006) The Application of a Convolution Neural Network on Face and License Plate Detection, in International Conference on Pattern Recognition, 552–555

  6. Chen Y, Nguyen TV, Kankanhalli M, Yuan J (2014) Audio Matters in Visual Attention, Circuits and Systems for Video Technology IEEE Transactions on 24(11), 1992

  7. Chen Y, Zhang L, Liu X, Chen C (2016) Flickr Circles: Aesthetic Tendency Discovery by Multi-View Regularized Topic Modeling, Information Sciences 372(C), 148

  8. Chen Y, Hu Y, Zhang L, Li P, Zhang C (2017) Engineering Deep Representations for Modeling Aesthetic Perception, IEEE Transactions on Cybernetics PP (99), 1

  9. Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks, Advances in neural information processing systems, 379–387

  10. Deb K, Jo K (2009) A Vehicle License Plate Detection Method for Intelligent Transportation System Applications 40(8), 689

  11. Dlagnekov (2004) License plate detection using adaboost, Computer Science and Engineering Department pp. 42–53

  12. Feng BY, Ren M, Zhang XY, Liu CL (2014) Effective license plate detection using fast candidate region selection and covariance feature based filtering, in IEEE International Conference on Advanced Video and Signal Based Surveillance, 1–60

  13. Girshick R, Donahue J, Darrell T, Malik J (2013) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, in IEEE Conference on Computer Vision and Pattern Recognition, 580–587

  14. He K, Zhang X, Ren S, Sun J (2015) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 37(9), 1904

  15. Hongliang B, Changping L (2004) A hybrid license plate extraction method based on edge statistics and morphology, in International Conference on Pattern Recognition, 831–834 Vol. 2

  16. Huang H, Gu M, Chao H (2008) An Efficient Method of License Plate Location in Natural-Scene Image, in International Conference on Fuzzy Systems and Knowledge Discovery, Fskd 2008, 18–20 October 2008, Jinan, Shandong, China, Proceedings, Volume, 15–19

  17. Hung KM, Hsieh CT (2010) A Real-Time Mobile Vehicle License Plate Detection and Recognition. Tamkang J Sci Eng 13(4):197

    MathSciNet  Google Scholar 

  18. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssde: Single shot multibox detector, European conference on computer vision, pp. 21–37

  19. Long J, Shelhamer E, Darrell T (2014) Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence PP (99), 640

  20. Luo L, Sun H, Zhou W, Luo L (2009) An Efficient Method of License Plate Location, in International Conference on Information Science and Engineering, 770–773

  21. Maglad KW (2012) A vehicle license plate detection and recognition system. J Comput Sci 8(3):310

    Article  Google Scholar 

  22. Parkhi OM, Vedaldi A, Zisserman A (2015) Deep Face Recognition, in British Machine Vision Conference, 41.1–41.12

  23. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence PP (99), 1

  24. Simonyan K, Zisserman A (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition, Computer Science

  25. Uijlings JR, Sande KE, Gevers T, Smeulders AW (2013) Selective Search for Object Recognition. Int J Comput Vis 104(2):154

    Article  Google Scholar 

  26. Wazalwar D, Oruklu E, Saniie J (2012) A Design Flow for Robust License Plate Localization and Recognition in Complex Scenes. J Trans Technol 02(1):13

    Google Scholar 

  27. Wu HHP, Chen HH, Wu RJ, Shen DF (2006) License Plate Extraction in Low Resolution Video, in International Conference on Pattern Recognition, 824–827

  28. Xia Y, Liu Z, Yan Y, Chen Y, Zhang L, Zimmermann R (2017) Media Quality Assessment by Perceptual Gaze-Shift Patterns Mining, IEEE Transactions on Multimedia PP (99), 1

  29. H. Zhang, W. Jia, X. He, Q. Wu (2006) Learning-Based License Plate Detection Using Global and Local Features, in International Conference on Pattern Recognition, 1102–1105

  30. Zitnick CL, Dollár P (2014) Edge Boxes: Locating Object Proposals from Edges 8693, 391

Download references

Acknowledgements

This work is supported by the National Nature Science Foundation of China (No. 61672201), the Natural Science Foundation of Anhui Province of China (No. 1708085MF158) and the China Scholarship Council (CSC No.201706695044).

Author information

Authors and Affiliations

  1. School of Computer and Information, Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei Engineering Research Center of Electric Power Data Application, Hefei University of Technology, Hefei, China

    Qiang Lu

  2. School of Computer and Information, Hefei University of Technology, Hefei, China

    Yu Liu, Jing Huang & Qingxin Hu

  3. Department of Science and Engineering, University of North Texas, Denton, TX, USA

    Xiaohui Yuan

Authors
  1. Qiang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaohui Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qingxin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingxin Hu.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Q., Liu, Y., Huang, J. et al. License plate detection and recognition using hierarchical feature layers from CNN. Multimed Tools Appl 78, 15665–15680 (2019). https://doi.org/10.1007/s11042-018-6889-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6889-1

Keywords

Search

Navigation