A convolutional neural network architecture for vehicle logo recognition

In order to achieve the significant vehicle logo recognition, a novel convolutional neural network(CNN) architecture is proposed. Considering the complexity of high dimensional data, we employ inception architecture and build a deep CNN network, which reduces the data dimensions and accelerates the computation of a large number of samples. We prepare a dataset to evaluate our algorithm, and obtain an overall accuracy of 99.02%. The comparison results show that our proposed algorithm outperforms linear support vector machine (SVM), LeNet-5, ImageNet and GoogLeNet in terms of accuracy improvement and computation-time reduction.