• 人工智能与模式识别 •

### 卷积神经网络在车辆识别中的应用

1. 1. 中南大学 信息科学与工程学院，长沙 410083
2. 湖南科技大学 计算机科学与工程学院，湖南 湘潭 411201
• 出版日期:2018-02-01 发布日期:2018-01-31

### Application of Convolutional Neural Network in Vehicle Recognition

PENG Qing1, JI Guishu1+, XIE Linjiang1, ZHANG Shaobo1,2

1. 1. School of Information Science and Engineering, Central South University, Changsha 410083, China
2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
• Online:2018-02-01 Published:2018-01-31

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.