Abstract
The systems based on image processing for vehicle type recognition is becoming more and fiercer. It plays an important role in traffic safety. In order to improve the problems that traditional Convolutional Neural Network has low accuracy of feature extraction from the low-resolution image, a novel model based on Deep Convolutional Neural Network (DCNN) was proposed. In this paper, our work mainly contains two aspects both extraction of feature dimension and recognition of vehicle image. Firstly, the learning way was introduced, and the raw image of vehicle subsampled with several different sizes was operated with the filter corresponding each channel in a way of convolution to extract the feature dimension of image. Secondly, the features dimension obtained from every channel were merged by a full connected layer. Eventually, features used to recognize the type of vehicle is got. The experiment shows that the architecture of DCNN model has a efficient performance on the recognition of vehicle image. Compared with the traditional algorithm of CNN, the results of experiment show that the mode of DCNN can achieve 97.6% accuracy and a higher precision is got.
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References
Takamoto, M., Ishikawa, H., Shimizu, K., et al.: New measurement method for very low liquid flow rates using ultrasound. Flow Meas. Instrum. 12(4), 267–273 (2001)
Warriach, E.U., Claudel, C.: Poster abstract: a machine learning approach for vehicle classification using passive infrared and ultrasonic sensors (2013)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)
Erhan, D., Szegedy, C., Toshev, A., et al.: Scalable object detection using deep neural networks. 2155–2162 (2013)
Hua, L., Xu, W., Wang, T., et al.: Vehicle recognition using improved sift and multi-view model. J. Xi’an Jiaotong Univ. 47(4), 92–99 (2013)
Taigman, Y., Yang, M., Ranzato, M., et al.: DeepFace: closing the gap to human-level performance in face verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708. IEEE (2014)
Rahati, S., Moravejian, R., Mohamad, E., et al.: Vehicle recognition using contourlet transform and SVM. In: Proceedings of the 5th International Conference on Information Technology: New Generations [S. l.], pp. 894–898. IEEE Press (2008)
Jun, Z.: Research on image retrieval based on fusion feature of AlexNet. Chongqing University of Posts and Telecommunications (2016)
Goodfellow, I.J., Warde-Farley, D., Mirza, M., et al.: Maxout networks. Comput. Sci. 28, 1319–1327 (2013)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichalet allocation. J. Mach. Learn. Res. 3(1), 993–1022 (2003)
Andrew, A.M.: An introduction to support vector machines and other kernel-based learning methods. 32(1), 1–28 (2001)
Fang, K., Wu, J., Zhu, J., et al.: A review of technologies on random forests. Stat. Inf. Forum 26(3), 32–38 (2011)
Zhang, H., Xun, F., Chen, J.: Face recognition based on multi-scale LBP. Comput. Appl. Softw. 29(1), 257–259 (2012)
Huang, F.C., Huang, S.Y., Ker, J.W., et al.: High-performance SIFT hardware accelerator for real-time image feature extraction. IEEE Trans. Circ. Syst. Video Technol. 22(3), 340–351 (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision & Pattern Recognition, pp. 886–893. IEEE Computer Society (2005)
Liu, D.: Deep convolutional neural networks for vehicle classification. Southwest Jiaotong University (2015)
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Shi, L., Wang, Y., Cao, Y., Wei, L. (2017). Vehicle Type Recognition Based on Deep Convolution Neural Network. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_42
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DOI: https://doi.org/10.1007/978-981-10-6388-6_42
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