摘要
针对现有车辆识别方法计算量大,提取特征复杂等问题,提出一种基于卷积神经网络(convolutional neural network,CNN)的车辆识别方法。构建卷积神经网络模型,分别使用不同的卷积核、网络层数、特征图数对网络进行训练;通过100次迭代的学习结果得到最优模型,提取隐含层所有特征,并结合支持向量机进行识别;系统分析了不同参数对测试正确率和样本均方误差的影响。实验结果显示,CNN+SVM在车辆识别中的准确率明显优于传统CNN、PCA+SVM、HOG+SVM、Wavelet+SVM,正确率为97.00%,分析了样本识别错误的原因以及今后需要改进的地方,为以后的研究指明了方向。
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.
出处
《计算机科学与探索》
CSCD
北大核心
2018年第2期282-291,共10页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金Nos.61632009
61472451
61402161~~
