Abstract
License plate recognition system is widely used in real life, such as toll stations, parking lots, crossroads, etc. These specific applications can effectively alleviate traffic jams, save labor costs, improve efficiency, but it also plays an important part of the intelligent transportation system. At present, most of the license plate recognition systems use computer vision and image processing technology for license plate character segmentation, then character recognition. The research goal of this paper is to use deep learning algorithm combined with transfer learning to improve the generalization ability and accuracy of license plate recognition system than traditional methods. In particular, first, we use the Xception network to train license plate data with weights randomly initialized. Next, Transfer Xception model for image classification with weights trained on ImageNet to this task and train license plate data again. Finally, we compare the accuracy and efficiency of license plate recognition system between these two models and other deep leaning networks.
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References
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. Off. J. Int. Neural Netw. Soc. 61, 85 (2015)
Torrey, L., Shavlik, J.: Transfer learning. In: International Encyclopedia of Education, 2nd edn. (2009)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. 1800–1807 (2016)
Yosinski, J., Clune, J., Bengio, Y., et al.: How transferable are features in deep neural networks? Eprint Arxiv 27, 3320–3328 (2014)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge (2014)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition, pp. 1–9. IEEE (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on International Conference on Machine Learning, pp. 448–456 (2015). JMLR.org
Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: Computer Vision and Pattern Recognition, pp. 2818–2826. IEEE (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., et al.: Inception-v4, inception-ResNet and the impact of residual connections on learning (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. 770–778 (2015)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)
Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.
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Zeng, Z., Gao, P., Sun, S. (2019). License Plate Recognition System Based on Transfer Learning. In: Sun, S. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2018. Lecture Notes in Electrical Engineering, vol 494. Springer, Singapore. https://doi.org/10.1007/978-981-13-1733-0_6
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DOI: https://doi.org/10.1007/978-981-13-1733-0_6
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