Vehicle-logo Recognition Based on Convolutional Neural Network with Multi-scale Parallel Layers

Abstract

For most of the vehicle logo recognition algorithms, the logo is difficult to be pinpointed, and the recognition is roughly to be done in bad environment. Even though the recognition accuracy of convolution neural network (CNN) is relatively high, it also needs a large number of samples. This paper proposes a multi-scale parallel convolution neural network (multi-scale parallel CNN) to recognize vehicle-logo and improves the existing vehicle detection method. The multi-scale convolution kernel is used to extract features from original data in a parallel way. This method can keep high accuracy in the condition of illumination change and noise pollution, and can adapt to the harsh environment. Experimental results show that the classification accuracy of the method is as high as 98.80{%} on our owe dataset and 99.80{%} on the dataset used in other paper, which demonstrates strong generalization ability of our proposed algorithm.