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Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition

Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition

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The training of deep convolutional neural network (CNN) for classification purposes is critically dependent on the expertise of hyper-parameters tuning. This study aims to minimise the user variability in training CNN by automatically searching and optimising the CNN architecture, particularly in the field of vehicle logo recognition system. For this purpose, the architecture and hyper-parameters of CNN were selected according to the implementation of the stochastic method of particle swarm optimisation on the training–testing data. After obtaining the optimised hyper-parameters, the CNN is fine-tuned and trained to ensure better network convergence and classification performance. In this study, a total of 14,950 vehicle logo images are divided into two independent training and testing sets. In addition, these images are segmented coarsely, thus the requirement of precise logo segmentation is obviated in this work. The learned features of the CNN were sufficiently discriminative to be classified using multiclass Softmax classifier. With implementation using a graphics processing unit (GPU), the computation time of the proposed method is acceptable for real-time application. The experimental results explicitly prove that the authors’ approach outperforms most of the state-of-the-art methods, achieving an accuracy of 99.1% over 13 vehicle manufacturers.

References

    1. 1)
      • 1. Huang, Y., Wu, R., Sun, Y., et al: ‘Vehicle logo recognition system based on convolutional neural networks with a pretraining strategy’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 19511960.
    2. 2)
      • 2. Munroe, D.T., Madden, M.G.: ‘Multi-class and single-class classification approaches to vehicle model recognition from images’. Proc. AICS, Portstewart, Ireland, 2005.
    3. 3)
      • 3. Petrovic, V.S., Cootes, T.F.: ‘Analysis of features for rigid structure vehicle type recognition’. BMVC, Kingston University, London, 2004.
    4. 4)
      • 4. Pearce, G., Pears, N.: ‘Automatic make and model recognition from frontal images of cars’. Eighth IEEE Int. Conf. Advanced Video and Signal-Based Surveillance (AVSS), Klagenfurt, Austria, 2011.
    5. 5)
      • 5. Psyllos, A., Anagnostopoulos, C.N., Kayafas, E.: ‘Vehicle model recognition from frontal view image measurements’, Comput. Stand. Interfaces, 2011, 33, (2), pp. 142151.
    6. 6)
      • 6. Psyllos, A.P., Anagnostopoulos, C.N.E., Kayafas, E.: ‘Vehicle logo recognition using a SIFT-based enhanced matching scheme’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (2), pp. 322328.
    7. 7)
      • 7. Llorca, D.F., Arroyo, R., Sotelo, M.A.: ‘Vehicle logo recognition in traffic images using HOG features and SVM’. 16th Int. IEEE Conf. Intelligent Transportation Systems (ITSC 2013), The Hague, Netherlands, 2013.
    8. 8)
      • 8. Hubel, D.H., Wiesel, T.N.: ‘Receptive fields, binocular interaction and functional architecture in the Cat's visual cortex’, J. Phys., 1962, 160, (1), pp. 106154o.102.
    9. 9)
      • 9. Khaw, H.Y., Soon, F.C., Chuah, J.H, et al: ‘Image noise types recognition using convolutional neural network with principal components analysis’, IET Image Process., Inst. Eng. Technol., 2017, 11, pp. 12381245.
    10. 10)
      • 10. Zang, D., Chai, Z.L., Zhang, J.Q., et al: ‘Vehicle license plate recognition using visual attention model and deep learning’, J. Electron. Imaging, 2015, 24, (3).
    11. 11)
      • 11. Dong, Z., Wu, Y., Pei, M., et al: ‘Vehicle type classification using a semisupervised convolutional neural network’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 22472256.
    12. 12)
      • 12. Fang, J., Zhou, Y., Yu, Y., et al: ‘Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture’, IEEE Trans. Intell. Transp. Syst., 2016, PP, (99), pp. 111.
    13. 13)
      • 13. Abdi, A.H., Luong, C., Tsang, T., et al: ‘Automatic quality assessment of echocardiograms using convolutional neural networks: feasibility on the apical four-chamber view’, IEEE Trans. Med. Imaging, 2017, 36, (6), pp. 12211230.
    14. 14)
      • 14. Bergstra, J., Bengio, Y.: ‘Random search for hyper-parameter optimization’, J. Mach. Learn. Res., 2012, 13, pp. 281305.
    15. 15)
      • 15. Bergstra, J.S., Bardenet, R., Bengio, Y., et al: ‘Algorithms for hyper-parameter optimization’, Adv. Neural Inf. Process. Syst., 2011, 24, pp. 25462554.
    16. 16)
      • 16. Garro, B.A., Vazquez, R.A.: ‘Designing artificial neural networks using particle swarm optimization algorithms’, Comput. Intell. Neurosci., 2015, 2015, p. 20.
    17. 17)
      • 17. Kennedy, J., Eberhart, R., Shi, Y.: ‘Swarm intelligence’ (Morgan Kaufmann Publishers, Massachusetts, United States, 2001).
    18. 18)
      • 18. Marini, F., Walczak, B.: ‘Particle swarm optimization (PSO). A tutorial’, Chemometr. Intell. Lab., 2015, 149, pp. 153165.
    19. 19)
      • 19. Belmecheri, F., Prins, C., Yalaoui, F., et al: ‘Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows’, J. Intell. Manuf., 2013, 24, (4), pp. 775789.
    20. 20)
      • 20. Salucci, M., Poli, L., Anselmi, N., et al: ‘Multifrequency particle swarm optimization for enhanced multiresolution Gpr microwave imaging’, IEEE Trans. Geosci. Remote Sens., 2017, 55, (3), pp. 13051317.
    21. 21)
      • 21. Marimuthu, S., Roomi, S.M.M.: ‘Particle swarm optimized fuzzy model for the classification of banana ripeness’, IEEE Sens. J., 2017, 17, (15), pp. 49034915.
    22. 22)
      • 22. Chou, S.K., Jiau, M.K., Huang, S.C.: ‘Stochastic set-based particle swarm optimization based on local exploration for solving the carpool service problem’, IEEE Trans. Cybern., 2016, 46, (8), pp. 17711783.
    23. 23)
      • 23. Pradeep Kumar, D., Ravi, V.: ‘Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network’, Appl. Soft Comput., 2017, 58, pp. 3552.
    24. 24)
      • 24. Zhou, Q., Zhang, W., Cash, S., et al: ‘Intelligent sizing of a series hybrid electric power-train system based on chaos-enhanced accelerated particle swarm optimization’, Appl. Energy, 2017, 189, pp. 588601.
    25. 25)
      • 25. Vedaldi, A., Lenc, K.: ‘MatConvNet: convolutional neural networks for MATLAB’. Proc. 23rd ACM Int. Conf. Multimedia, Brisbane, Australia, 2015, pp. 689692.
    26. 26)
      • 26. Bousquet, O., Bottou, L.: ‘The tradeoffs of large scale learning’. Advances in Neural Information Processing Systems, Vancouver, Canada, 2008.
    27. 27)
      • 27. Burkhard, T., Minich, A., Li, C.: ‘Vehicle logo recognition and classification: feature descriptors vs. shape descriptors’, EE368 Final Project Spring, 2011.
    28. 28)
      • 28. Psyllos, A., Anagnostopoulos, C.N., Kayafas, E.: ‘M-SIFT: a new method for vehicle logo recognition’. 2012 IEEE Int. Conf. Vehicular Electronics and Safety (ICVES 2012), Istanbul, Turkey, 2012.
    29. 29)
      • 29. Farajzadeh, N., Rezaei, N.S.: ‘Vehicle logo recognition using image matching and textural features’. Scientific Cooperations Int. Workshops on Electrical and Computer Engineering Subfields, Istanbul, Turkey, 2014.
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