关键词: 车辆识别, 深度学习, 卷积神经网络（CNN）, 特征提取, 支持向量机（SVM）

Abstract: Aiming at the problems of excessive calculation and complex feature extraction of existing vehicle recognition methods, this paper proposes a vehicle recognition method based on convolutional neural network (CNN). Firstly, this paper constructs a convolutional neural network model, which is trained with different size of convolution kernel, different number of network layers and different number of feature maps. Secondly, this paper obtains the optimal model through 100 iterations learns, from which to extract all features of hidden layer and combined with support vector machines (SVM) to proceed with recognition. Finally, this paper systematically analyzes the influence of different parameters on the accuracy and mean square error. The experimental results show that in vehicle recognition CNN+SVM had a high accuracy rate as compared to the traditional CNN, PCA+SVM, HOG+SVM and Wavelet+SVM, whose accuracy rate is 97.00%. This paper focuses on analyzing the cause for errors in samples and necessary modifications to be done hereafter.

Key words: vehicle recognition, deep learning, convolutional neural network (CNN), feature extracting, support vector machine (SVM)