E-MobileViT: a lightweight model for traffic sign recognition
Abstract Traffic sign recognition is crucial for intelligent transportation and autonomous driving, ensuring road safety and efficient traffic management. This paper proposes a lightweight enhanced MobileViT model (E-MobileViT). It is based on the MobileViT model, combining the advantages of CNN and...
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Industrial Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44244-025-00024-2 |
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| _version_ | 1849389929799352320 |
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| author | Shiqi Song Xinfeng Ye Sathiamoorthy Manoharan |
| author_facet | Shiqi Song Xinfeng Ye Sathiamoorthy Manoharan |
| author_sort | Shiqi Song |
| collection | DOAJ |
| description | Abstract Traffic sign recognition is crucial for intelligent transportation and autonomous driving, ensuring road safety and efficient traffic management. This paper proposes a lightweight enhanced MobileViT model (E-MobileViT). It is based on the MobileViT model, combining the advantages of CNN and Transformer. We integrate Efficient Local Attention (ELA) and Convolutional Block Attention Module (CBAM) mechanisms in the model to improve feature extraction. The proposed model improves the feature fusion structure and significantly reduces the number of model parameters. We evaluated the model on the German Traffic Sign Recognition Benchmark (GTSRB), Belgian Traffic Signs Database (BTSD), and China Traffic Signs Database (TSRD) datasets and its accuracy reaches 99.61%, 99.26% and 97.34%, respectively, which outperforms traditional and advanced models. We confirmed the key role of ELA and CBAM mechanisms through ablation experiments. With fewer parameters than mainstream models, our E-MobileViT model is suitable for resource-constrained environments such as mobile devices, providing a balanced solution for traffic sign recognition tasks. |
| format | Article |
| id | doaj-art-9d81c02b6c7d441ebb6f9992ec3cb29c |
| institution | Kabale University |
| issn | 2731-667X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Industrial Artificial Intelligence |
| spelling | doaj-art-9d81c02b6c7d441ebb6f9992ec3cb29c2025-08-20T03:41:49ZengSpringerIndustrial Artificial Intelligence2731-667X2025-03-013111510.1007/s44244-025-00024-2E-MobileViT: a lightweight model for traffic sign recognitionShiqi Song0Xinfeng Ye1Sathiamoorthy Manoharan2School of Computer Science, The University of AucklandSchool of Computer Science, The University of AucklandSchool of Computer Science, The University of AucklandAbstract Traffic sign recognition is crucial for intelligent transportation and autonomous driving, ensuring road safety and efficient traffic management. This paper proposes a lightweight enhanced MobileViT model (E-MobileViT). It is based on the MobileViT model, combining the advantages of CNN and Transformer. We integrate Efficient Local Attention (ELA) and Convolutional Block Attention Module (CBAM) mechanisms in the model to improve feature extraction. The proposed model improves the feature fusion structure and significantly reduces the number of model parameters. We evaluated the model on the German Traffic Sign Recognition Benchmark (GTSRB), Belgian Traffic Signs Database (BTSD), and China Traffic Signs Database (TSRD) datasets and its accuracy reaches 99.61%, 99.26% and 97.34%, respectively, which outperforms traditional and advanced models. We confirmed the key role of ELA and CBAM mechanisms through ablation experiments. With fewer parameters than mainstream models, our E-MobileViT model is suitable for resource-constrained environments such as mobile devices, providing a balanced solution for traffic sign recognition tasks.https://doi.org/10.1007/s44244-025-00024-2MobileViTTraffic sign recognitionAttention mechanismsTransformer |
| spellingShingle | Shiqi Song Xinfeng Ye Sathiamoorthy Manoharan E-MobileViT: a lightweight model for traffic sign recognition Industrial Artificial Intelligence MobileViT Traffic sign recognition Attention mechanisms Transformer |
| title | E-MobileViT: a lightweight model for traffic sign recognition |
| title_full | E-MobileViT: a lightweight model for traffic sign recognition |
| title_fullStr | E-MobileViT: a lightweight model for traffic sign recognition |
| title_full_unstemmed | E-MobileViT: a lightweight model for traffic sign recognition |
| title_short | E-MobileViT: a lightweight model for traffic sign recognition |
| title_sort | e mobilevit a lightweight model for traffic sign recognition |
| topic | MobileViT Traffic sign recognition Attention mechanisms Transformer |
| url | https://doi.org/10.1007/s44244-025-00024-2 |
| work_keys_str_mv | AT shiqisong emobilevitalightweightmodelfortrafficsignrecognition AT xinfengye emobilevitalightweightmodelfortrafficsignrecognition AT sathiamoorthymanoharan emobilevitalightweightmodelfortrafficsignrecognition |