Ensemble deep learning based tire classification with CNNs and ViTs

Due to the close similarity in tire textures, it is challenging to predict the type of a tire with a single deep learning model. Therefore, an ensemble learning based tire classification method is proposed in this paper, which leverages the advantages local perception in Convolutional Neural Networks (CNNs) and global attention in Vision Transformers (ViTs). Initially, individual networks based on CNNs and ViTs are independently trained. Subsequently, the backbone networks of these models are frozen, and the features obtained from different models are concatenated. The concatenated features are then fed into a feed forward network for prediction. The experimental results demonstrate that the proposed ensemble model exhibits higher accuracy and stronger generalization capability in tire classification tasks. Compared to individual predictions with single model based on CNNs and ViTs, the accuracy is improved by more than 18.51% and 8.05%, respectively.