Ensemble deep learning based tire classification with CNNs and ViTs

Lang He; Shiyun Li

doi:10.1117/12.3026703

27 March 2024 Ensemble deep learning based tire classification with CNNs and ViTs

Lang He, Shiyun Li

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310527 (2024) https://doi.org/10.1117/12.3026703
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lang He and Shiyun Li "Ensemble deep learning based tire classification with CNNs and ViTs", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310527 (27 March 2024); https://doi.org/10.1117/12.3026703

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