Vehicle detection and classification based on convolutional neural network

Abstract

Deep learning has emerged as a hot topic due to extensive application and high accuracy. In this paper this efficient method is used for vehicle detection and classification. We extract visual features from the activation of a deep convolutional network, large-scale sparse learning and other distinguishing features in order to compare their accuracy. When compared to the leading methods in the challenging ImageNet dataset, our deep learning approach obtains highly competitive results. Through the experiments with in short of training data, the features extracted by deep learning method outperform those generated by traditional approaches.