Deep learning has many important applications in autonomous vehicle classification. However, due to the similarity between different types of vehicles, the deep learning model is faced with a certain degree of challenge in classifying vehicles. If the deep learning method can be improved to improve the accuracy of vehicle classification method, it will be very helpful for the practical application of autonomous driving. In this paper, ResNet34 deep learning model is selected as the backbone network, and FcaNet and several spatial attention mechanisms are added to improve the model. We tested our proposed approach on a data set containing 10 different types of vehicles. The test results on the vehicle classification data set show that the classification accuracy of the improved scheme reaches 79.0%, which is higher than the 75.42% of the original ResNet34 model. The experimental results show that the method of adding multiple attention mechanisms to ResNet deep learning network is helpful to improve the classification accuracy of different vehicles.
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.
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