In recent years, vehicle recognition has become an important application in intelligent traffic monitoring and management. In this paper, we proposed a deep convolution neural network which is no less than nine layers. A vehicle data set is employed which is collected from multiple perspectives and the deep learning framework Caffe is used to verify the proposed algorithm. Comparing with traditional vehicle recognition based on machine learning which needs vehicle location and has low accuracy of shortcomings, the proposed model uses deep convolution neural network has a better performance.
Luo, X., Shen, R., Hu, J., Deng, J., Hu, L., & Guan, Q. (2017). A Deep Convolution Neural Network Model for Vehicle Recognition and Face Recognition. In Procedia Computer Science (Vol. 107, pp. 715–720). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.03.153