classification is an important part of vision systems and has several applications like autonomous cars and surveillance. This is a challenging task because computers see images differently from humans. This paper used the MobileNet model for training the data and tested it on an Android device. This model is lightweight and efficient compared with previous developed models. This was inspired by the sample code from Google Codelabs. Experiment results show that the Android application can accurately classify the type of vehicle in real time.
Keywords: convolutional neural network, deep learning, MobileNet, vehicle classification[1] A. Ferdowsi, U. Challita and W. Saad, “Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems,” arXiv:1712.04135v1, December 2017.
[2] R. K. C. Billones, A. A. Bandala, E. Sybingco, L. A. G. Lim and E. P. Dadios, “Intelligent system architecture for a vision based contactless apprehension of traffic violations,” 2016 IEEE Region 10 Conference (TENCON), pp. 1871 - 1874, 2016.
[3] R. K. C. Billones, A. A. Bandala, E. Sybingco, L. A. G. Lim, A. D. Fillone and E. P. Dadios, “Vehicle Detection and Tracking using Corner Feature Points and Artificial Neural Networks for a Vision Based Contactless Apprehension System,” Computing Conference 2017, pp. 688-691, 2017.
[4] P. Kamavisdar, S. Saluja and S. Agrawal, “A Survey on Image Classification Approaches and Techniques,” International Journal of Advanced Research in Computer and Communication Engineering, vol. II, no. 1, pp. 1005-1009, January 2013.
[5] A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in neural information processing systems, 2012.
[6] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks For Large-Scale Image Recognition,” in ICLR, 2015.
[7] K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE Conference Publication, pp. 1-12, 2015.
[8] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, “Going Deeper with Convolutions,” arXiv:1409.4842v1, pp. 1-12, September 2014.
[9] F. Chollet, “Xception: Deep Learning with Depth Wise Separable Convolutions,” IEEE Conference Publication, pp. 1-8, 2017.
[10] G. Huang, Z. Liu, L. Van der Maaten and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
[11] R. Girshick, J. Donahue, T. Darrell and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” arXiv:1311.2524v5 [cs.CV], pp. 1-21, 2014. [12] R. Girshick, “Fast R-CNN,” arXiv:1504.08083v2 [cs.CV], 2015.
[13] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You Only Look Once:Unified, Real-Time Object Detection,” arXiv:1506.02640v5 [cs.CV], pp. 1-10, 2016.
[14] J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” arXiv:1612.08242v1 [cs.CV], pp. 1-9, 2016.
[15] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu and A. C. Berg, “SSD: Single Shot MultiBox Detector,” arXiv:1512.02325v5, pp. 1-17, 2016.
[16] A. G. Howard, M. Zhu, B. Chen and D. Kalenichenko, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision,” 2017.
[17] V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings of the 27th international conference on machine learning (ICML-10), 2010.
[18] S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv:1502.03167v3, pp. 1-11, March 2015.
[19] Codelabs, Google, “Tensorflow for Poets 2,” December 2017. [Online]. Available: https://github.com/googlecodelabs/ tensorflow-for-poets-2.
[20] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu and X. Zheng, “TensorFlow: A system for large-scale machine learning,” arXiv:1605.08695v2, pp. 1-18, May 2016.