access icon free Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features

© The Institution of Engineering and Technology
Received 24/04/2017, Accepted 19/11/2017, Revised 26/10/2017, Published 28/11/2017

License plate recognition (LPR) is an important component of intelligent transportation systems. Compared with letters and numbers, Chinese characters contain more information, making automatic recognition more difficult. Accurate Chinese LPR (CLPR) is determined by three factors: training dataset, feature extractor, and classifier. Most license plates with benchmark dataset contain only letters and numbers; thus, the authors build a large dataset for CLPR. Convolutional neural networks (CNNs) can be used to extract inherent image features, on all levels of abstraction. CNNs can be used for classification if they have a sufficient number of fully connected layers. This implies that CNNs must be trained using gradient descent-based methods, which often yields sub-optimal results. Extreme learning machines (ELMs) demonstrate impressive performance on classification, with good generalisation. Therefore, the authors propose a novel deep architecture for CLPR which combines a CNN and an ELM. Firstly, a CNN without fully connected layers, working as a feature extractor, learns deep features associated with characters in written Chinese. Then, a kernel-based ELM (KELM) classifier, which accepts CNN features as input, is utilised for classification. Compared with CNNs that use Softmax, support vector machines and ELMs, the CNN that uses KELM yields competitive results in a shorter training time.

Inspec keywords: object recognition; traffic engineering computing; image classification; feature extraction; learning (artificial intelligence); feedforward neural nets

Other keywords: support vector machines; convolutional neural networks; Chinese vehicle license plate recognition; ELM; kernel-based extreme learning machine; Softmax; feature extractor; deep convolutional features; CLPR; classification; KELM classifier; CNN

Subjects: Image recognition; Neural computing techniques; Traffic engineering computing; Computer vision and image processing techniques; Knowledge engineering techniques

References

    1. 1)
      • 17. Rikhtegar, A., Pooyan, M., Manzuri-Shalmani, M.T.: ‘Genetic algorithm-optimised structure of convolutional neural network for face recognition application’, IET Comput. Vis., 2016, 10, (6), pp. 559–566.
    2. 2)
      • 41. Sangari, A., Sethares, W.: ‘Convergence analysis of two loss functions in Soft-Max regression’, IEEE Trans. Signal Process., 2016, 64, (5), pp. 1280–1288.
    3. 3)
      • 22. Huang, G.-B., Zhou, H.-M., Ding, X.-J., et al: ‘Extreme learning machine for regression and multiclass classification’, IEEE Trans. Syst. Man Cybern. B, 2012, 42, (2), pp. 513–529.
    4. 4)
      • 11. Sarikaya, R., Hinton, G.E., Deoras, A.: ‘Application of deep belief networks for natural language understanding’, IEEE/ACM Trans. Audio Speech Lang. Process., 2016, 22, (4), pp. 778–784.
    5. 5)
      • 42. Delgado, M.F., Cernadas, E., Barro, S., et al: ‘Do we need hundreds of classifiers to solve real world classification problems’, J. Mach. Learn. Res., 2014, 2014, (15), pp. 3133–3181.
    6. 6)
      • 18. Zeng, Y.-J., Xu, X., Fang, Y.-Q., et al: ‘Traffic sign recognition using deep convolutional networks and extreme learning machine’. Proc. Int. Conf. Intelligence Science and Big Data Engineering, Suzhou, October 2015, pp. 272–280.
    7. 7)
      • 5. Hsu, G.-S., Chen, J.-C., Chung, Y.-Z., et al: ‘Application-oriented license plate recognition’, IEEE Trans. Intell. Transp. Syst., 2013, 62, (2), pp. 552–561.
    8. 8)
      • 15. Zhang, X.-Y., Zou, J.-H., He, K.-M., et al: ‘Accelerating very deep convolutional networks for classification and detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (10), pp. 1943–1955.
    9. 9)
      • 12. Huang, G.-B., Bai, Z., Kasun, L.L.C., et al: ‘Local receptive field based extreme learning machine’, IEEE Comput. Intell. Mag., 2015, 10, (2), pp. 18–29.
    10. 10)
      • 37. Yang, Y., Zhang, W.-G., Guo, P.: ‘Realization for Chinese vehicle license plate recognition based on computer vision and fuzzy neural network’. Proc. Int. Conf. Display and Photonics, Nanjing, China, July 2010, pp. 77491G–77496G.
    11. 11)
      • 39. Ashtari, A.H., Nordin, M.J., Fathy, M.: ‘An Iranian license plate recognition system based on color features’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (4), pp. 1690–1705.
    12. 12)
      • 38. Yuan, X., Hao, X.-L., Chen, H.-J., et al: ‘Robust traffic sign recognition based on color global and local oriented edge magnitude patterns’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (4), pp. 1466–1477.
    13. 13)
      • 25. Du, S., Ibrahim, M., Shehata, M., et al: ‘Automatic license plate recognition (ALPR): a state-of-the-art review’, IEEE Trans. Circuits Syst. Video Technol., 2013, 23, (2), pp. 311–325.
    14. 14)
      • 34. Everingham, M., Van Gool, L., Williams, C.K.I., et al: ‘The PASCAL visual object classes challenge’, Int. J. Comput. Vis., 2010, 88, (2), pp. 303–338.
    15. 15)
      • 16. Geng, M.-Y., Wang, Y.-W., Tian, Y.-H., et al: ‘CNUSVM: hybrid CNN-uneven SVM model for imbalanced visual learning’. Proc. Int. Conf. Multimedia Big Data, Taipei, August 2016, pp. 186–193.
    16. 16)
      • 3. Lu, Q.-B., Zhou, W.-G., Fang, L., et al: ‘Robust blur kernel estimation for license plate images from fast moving vehicles’, IEEE Trans. Image Process., 2016, 25, (5), pp. 2311–2322.
    17. 17)
      • 8. Chen, X.-Y., Peng, X.-Y., Li, J.-B., et al: ‘Overview of deep kernel learning based techniques and applications’, J. Netw. Intell., 2016, 1, (3), pp. 82–97.
    18. 18)
      • 7. Lawlor, S., Sider, T., Eluru, N., et al: ‘Detecting convoys using license plate recognition data’, IEEE Trans. Signal Inf. Process. Over Netw., 2016, 2, (3), pp. 391–405.
    19. 19)
      • 4. Asif, M.R., Chun, Q., Hussain, S., et al: ‘Multiple license plate detection for Chinese vehicles in dense traffic scenarios’, IEEE Trans. Intell. Transp. Syst., 2016, 10, (8), pp. 535–544.
    20. 20)
      • 6. Rajput, H., Som, T., Kar, S.: ‘An automated vehicle license plate recognition system’, IEEE Comput. Soc., 2015, 48, (8), pp. 56–61.
    21. 21)
      • 40. Karasuyama, M., Tackeuchi, I.: ‘Nonlinear regularization path for quadratic loss support vector machines’, IEEE Trans. Neural Netw., 2011, 22, (10), pp. 1613–1625.
    22. 22)
      • 20. Huang, Z.-Y., Yu, Y.-L., Gu, J., et al: ‘An efficient method for traffic sign recognition based on extreme learning machine’, IEEE Trans. Cybern., 2017, 47, (4), pp. 920–933.
    23. 23)
      • 30. Gou, C., Wang, K.-F., Yao, Y.-J., et al: ‘Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (4), pp. 1096–1107.
    24. 24)
      • 31. Wang, N.-G., Zhu, X.-W., Zhang, J.: ‘License plate segmentation and recognition of Chinese vehicle based on BPNN’. Proc. Int. Conf. IEEE Computational Intelligence and Security, Wuxi, China, December 2016, pp. 403–406.
    25. 25)
      • 32. Chen, J.-R.: ‘Chinese license plate identification based on Android platform’. Proc. Int. Conf. IEEE Computational Intelligence and Communication Technology, Ghaziabad, India, February 2017, pp. 1–5.
    26. 26)
      • 9. Chen, X.-Y., Xiang, S.-M., Liu, L.-C., et al: ‘Vehicle detection in satellite images by hybrid deep convolutional neural networks’, IEEE Geosci. Remote Sens. Lett., 2014, 11, (10), pp. 1797–1801.
    27. 27)
      • 27. Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Loumos, V., et al: ‘A license plate-recognition algorithm for intelligent transportation system applications’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (3), pp. 377–392.
    28. 28)
      • 28. Wen, Y., Lu, Y., Yan, J.-Q., et al: ‘An algorithm for license plate recognition applied to intelligent transportation system’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (3), pp. 830–845.
    29. 29)
      • 33. Wah, C., Branson, S., Welinder, P., et al: ‘The Caltech-UCSD birds-200-2011 dataset’, California Institute of Technology, 2011, pp. 20111026–120541847.
    30. 30)
      • 35. Niu, X.-X., Suen, C.-Y.: ‘A novel hybrid CNN–SVM classifier for recognizing handwritten digits’, Pattern Recognit., 2012, 2012, (45), pp. 1318–1325.
    31. 31)
      • 13. Lecun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 2278–2324.
    32. 32)
      • 21. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: ‘Extreme learning machine: a new learning scheme of feedforward neural networks’. Proc. Int. Joint Conf. Neural Networks, Budapest, July 2004, pp. 985–990.
    33. 33)
      • 26. Chang, S.-L., Chen, L.-S., Chung, Y.-C., et al: ‘Automatic license plate recognition’, IEEE Trans. Intell. Transp. Syst., 2004, 5, (1), pp. 42–53.
    34. 34)
      • 14. Liu, M.-C., Shi, J.-X., Li, Z., et al: ‘Towards better analysis of deep convolutional neural networks’, IEEE Trans. Vis. Comput. Graph., 2017, 23, (1), pp. 91–100.
    35. 35)
      • 2. Wen, Y., Lu, Y.-L., Yan, J.-Q., et al: ‘An algorithm for license plate recognition applied to intelligent transportation system’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (3), pp. 830–840.
    36. 36)
      • 36. Tissera, M.D., McDonnell, M.D.: ‘Deep extreme learning machines: supervised autoencoding architecture for classification’, Neurocomputing, 2016, 2016, (174), pp. 42–49.
    37. 37)
      • 19. Zeng, Y.-J., Xu, X., Shen, S.-Y., et al: ‘Traffic sign recognition using kernel extreme learning machine with deep perceptual features’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (6), pp. 1647–1653.
    38. 38)
      • 23. Huang, G.-B.: ‘An insight into extreme learning machines: random neurons, random features and kernels’, Cogn. Comput., 2014, 6, (3), pp. 376–390.
    39. 39)
      • 10. Weng, R.-L., Lu, J.-W., Tan, Y.-P., et al: ‘Learning cascaded deep auto-encoder network for face alignment’, IEEE. Trans. Multimedia, 2016, 18, (10), pp. 2066–2078.
    40. 40)
      • 24. Tang, J.-X., Deng, C.-W., Huang, G.-B.: ‘Extreme learning machine for multilayer perceptron’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, (4), pp. 809–821.
    41. 41)
      • 29. Dun, J.-Y., Zhang, S.-Y., Ye, X.-Z., et al: ‘Chinese license plate localization in multi-lane with complex background based on concomitant colors’, IEEE Trans. Intell. Transp. Syst. Mag., 2015, 7, (3), pp. 51–61.
    42. 42)
      • 1. Tian, B., Morris, B.T., Tang, M., et al: ‘Hierarchical and networked vehicle surveillance in ITS: a survey’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (1), pp. 25–48.
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