结合卷积神经网络多层特征和支持向量机的车辆识别

Vehicle Recognition Based on Multi-Layer Features of Convolutional Neural Network and Support Vector Machine

车辆识别方法计算量大,提取的特征复杂,且传统神经网络利用端层特征进行分类导致特征不全面,为此提出了一种结合卷积神经网络(CNN)多层特征和支持向量机(SVM)的车辆识别方法。该方法在传统AlexNet模型基础上构建卷积神经网络模型,通过分析参数变化对测试正确率的影响得到最优车辆识别模型;提取多层车辆特征图,采用串行融合方法与主成分分析降维技术将其构成一个具有多属性的车辆特征向量,以增强特征全面性,减少计算量;利用SVM分类器代替CNN的输出层实现车辆识别,以提高模型泛化能力与纠错能力。实验结果表明,相比传统方法,所提方法在分类精度和识别速度方面都有显著提高,且具有良好的稳健性。

Vehicle recognition has a large amount of computation and complex extracted features, while the traditional neural network has incomplete features defined by end-layer features. Therefore, we propose a new vehicle recognition method based on multi-layer features of the convolutional neural network (CNN) and support vector machine (SVM). Firstly, the CNN model is constructed based on the traditional AlexNet model, while the optimal vehicle recognition model is obtained by analyzing the effect of parameter change on the accuracy. Further, the multi-layer vehicle feature map is extracted, and a multi-attribute vehicle feature vector is formed by the serial fusion method and the principal component analysis to enhance the comprehensiveness of the feature and reduce the computational complexity. Finally, vehicle recognition is realized by using the SVM classifier instead of the output layer of CNN , which improves the generalization and error-correction abilities of the model. The experimental results reveal that the proposed method has remarkable performance in classification accuracy and recognition speed;additionally, it has better robustness, compared with the traditional methods.