基于多尺度跃层卷积神经网络的精细车型识别

摘要

In order to solve the problem in vehicle make-and-model recognition that the features are not representative and the recognition accuracy is low,a method based on multiscale layer-skipping convolutional neural network(CNN) is proposed.First,extracting local features and global features by multiscale layer-skipping convolutional kernels,and then train the Sofimax classifiers.An adaptive fusion method is used to adjust the contribution of different networks.The recognition results of some single scale layer-skipping convolutional neural networks are fused,and then got the final classification models.The recognition accuracy of the model is 97.59％.The experimental results show that the multiscale layer-skipping convolutional neural network is suitable for fine vehicle recognition,and can improve the accuracy of recognition.%为解决精细车型识别中特征不具有代表性,且识别准确率低的问题,提出了基于多尺度跃层卷积神经网络(CNN)的车型识别方法.通过多个不同尺度的跃层卷积神经网络,提取适用于精细车型识别的低层局部特征和高层全局特征,并分别训练Softmax分类器.利用自适应方式融合方法,将多个单一尺度跃层卷积神经网络的识别结果进行融合,调整不同网络对识别结果的贡献.实验中车型识别准确率达到97.59％.实验结果表明多尺度跃层卷积神经网络适用于精细的车型识别,并能提高识别的准确率.