http://iet.metastore.ingenta.com
1887

New approach to vehicle license plate location based on new model YOLO-L and plate pre-identification

New approach to vehicle license plate location based on new model YOLO-L and plate pre-identification

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Currently, the conventional license plate location method fails to detect the license plate under complex road environments such as severe weather conditions and viewpoint changes. Besides, it is difficult for license plate location method based on machine learning to precisely locate the area of license plate. Moreover, license plate location method may incorrectly detect similar objects such as billboards and road signs as license plates. To alleviate these problems, this article proposes a new approach to vehicle license plate location based on new model YOLO-L and plate pre-identification. The new model improves in two aspects to precisely locate the area of license plate. First, it uses k-means++ clustering algorithm to select the best number and size of plate candidate boxes. Second, it modifies the structure and depth of YOLOv2 model. Plate pre-identification algorithm can effectively distinguish license plates from similar objects. The experimental results show that authors’ proposed method not only achieves a precision of 98.86% and a recall of 98.86%, which outperforms the existing methods, but also has high efficiency in real time.

References

    1. 1)
      • 1. Gou, C., Wang, K., Yao, Y., et al: ‘Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (4), pp. 10961107.
    2. 2)
      • 2. Türkyılmaz, İ., Kaçan, K.: ‘License plate recognition system using artificial neural networks’, ETRI J., 2017, 39, (2), pp. 163172.
    3. 3)
      • 3. Xing, J., Li, J., Xie, Z., et al: ‘Research and implementation of an improved radon transform for license plate recognition’. IEEE Int. Conf. on Intelligent Human Machine Systems and Cybernetics, Hangzhou, China, 2016, pp. 4548.
    4. 4)
      • 4. Abolghasemi, V., Ahmadyfard, A.: ‘An edge-based color-aided method for license plate detection’, Image Vis. Comput., 2009, 27, (8), pp. 11341142.
    5. 5)
      • 5. Deb, K., Jo, K.H.: ‘HSI color based vehicle license plate detection’. Int. Conf. on Control, Automation and Systems, Seoul, South Korea, 2008, pp. 687691.
    6. 6)
      • 6. Guo, J., Liu, Y.: ‘License plate localization and character segmentation with feedback self-learning and hybrid binarization techniques’, IEEE Trans. Veh. Technol., 2008, 57, (3), pp. 14171424.
    7. 7)
      • 7. Zheng, D., Zhao, Y., Wang, J.: ‘An efficient method of license plate location’, Pattern Recognit. Lett., 2005, 26, (15), pp. 24312438.
    8. 8)
      • 8. Reji, P.I., Dharun, V.S.: ‘License plate detection and recognition using vertical based edge detection algorithm and radial basis function neural network’, Indian J. Sci. Technol., 2015, 8, (26), pp. 15.
    9. 9)
      • 9. Yu, S., Li, B., Zhang, Q., et al: ‘A novel license plate location method based on wavelet transform and EMD analysis’, Pattern Recognit., 2015, 48, (1), pp. 114125.
    10. 10)
      • 10. Rajput, H., Som, T., Kar, S.: ‘Using radon transform to recognize skewed images of vehicular license plates’, Computer, 2016, 49, (1), pp. 5965.
    11. 11)
      • 11. Ojala, T., Pietikäinen, M., Mäenpää, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971987.
    12. 12)
      • 12. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. IEEE Conf. Computer Vision and Pattern Recognition, Kauai, USA, 2001, pp. 511518.
    13. 13)
      • 13. Han, B., Lee, J., Lim, K., et al: ‘Real-time license plate detection in high-resolution videos using fastest available cascade classifier and core patterns’, ETRI J., 2015, 37, (2), pp. 251261.
    14. 14)
      • 14. Zhang, X, Shen, P, Song, J, et al: ‘An algorithm combined with color differential models for license plate location’, Neurocomputing., 2016, 212, pp. 2235.
    15. 15)
      • 15. Wang, R., Sang, N., Huang, R., et al: ‘License plate detection using gradient information and cascade detectors’, Opt.–Int. J. Light Electron Opt., 2014, 125, (1), pp. 186190.
    16. 16)
      • 16. Liu, W., Anguelov, D., Erhan, D., et al: ‘SSD: single shot multibox detector’. European Conf. on Computer Vision, Amsterdam, Netherlands, 2016, pp. 2137.
    17. 17)
      • 17. Ren, S., He, K., Girshick, R., et al: ‘Faster R-CNN: towards real-time object detection with region proposal networks’. Int. Conf. on Neural Information Processing Systems, Istanbul, Turkey, 2015, pp. 9199.
    18. 18)
      • 18. Redmon, J., Divvala, S., Girshick, R., et al: ‘You only look once: unified, real-time object detection’. IEEE Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2016, pp. 779788.
    19. 19)
      • 19. Redmon, J., Farhadi, A.: ‘YOLO9000: better, faster, stronger’. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, USA, 2017, pp. 65176525.
    20. 20)
      • 20. Li, H., Shen, C.: ‘Reading car license plates using deep convolutional neural networks and LSTMs’, arXiv preprint arXiv:1601.05610, 2016.
    21. 21)
      • 21. Rafique, M.A., Pedrycz, W., Jeon, M.: ‘Vehicle license plate detection using region-based convolutional neural networks’, Soft Comput., 2018, 22, (19), pp. 64296440.
    22. 22)
      • 22. Xie, L., Ahmad, T., Jin, L., et al: ‘A new CNN-based method for multi-directional car license plate detection’, IEEE Trans. Intell. Transp. Syst., 2018, 19, (2), pp. 507517.
    23. 23)
      • 23. Arthur, D., Vassilvitskii, S.: ‘K-means++: the advantages of careful seeding’. Eighteenth Acm-Siam Symp. on Discrete Algorithms, Philadelphia, USA, 2007, pp. 10271035.
    24. 24)
      • 24. Hsu, G.S., Chen, J.C., Chung, Y.Z.: ‘Application-oriented license plate recognition’, IEEE Trans. Veh. Technol., 2013, 62, (2), pp. 552561.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.6449
Loading

Related content

content/journals/10.1049/iet-ipr.2018.6449
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address