A vehicle classification method based on deep learning and data augmentation

Abstract

At present, deep learning has been applied in a lot of autonomous driving, and vehicle classification based on deep learning is an important content. Although deep learning methods have proven to be more effective at classifying vehicles than traditional machine learning methods. However, in practice, due to certain similar characteristics among different types of vehicles, the accuracy of vehicle classification based on deep learning method is not high enough. In order to improve the effectiveness of deep learning in the field of vehicle classification, this paper studies from the data side. The method of this paper is to propose a novel data augmentation method according to the characteristics of vehicles and combine with ResNet34 model. After experimental verification, the results of the test set show that the classification accuracy of the ResNet34 model after data augmentation in this paper is 80.0%, higher than the classification accuracy of 75.42% without data augmentation. The above results show that the data augmentation method proposed in this paper is very effective for vehicle classification problems and can be used in conjunction with deep learning model.