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Open Access

Peer-reviewed

Research Article

Res2Net-based multi-scale and multi-attention model for traffic scene image classification

Guanghui Gao,

Roles Conceptualization, Data curation, Investigation, Methodology, Software, Validation, Writing – original draft

Affiliation School of Computer Science and Technology, Xinjiang University, Urumqi, China
⨯
Yining Guo,

Roles Formal analysis, Validation, Writing – review & editing

Affiliation School of Computer Science and Technology, Xinjiang University, Urumqi, China
⨯
Lumei Zhou,

Roles Project administration, Resources, Writing – review & editing

Affiliation School of Computer Science and Technology, Xinjiang University, Urumqi, China
⨯
Li Li,

Roles Project administration, Writing – review & editing

Affiliation School of Computer Science and Technology, Xinjiang University, Urumqi, China
⨯
Gang Shi

Roles Investigation, Writing – review & editing

* E-mail: shigang@xju.edu.cn

Affiliation School of Computer Science and Technology, Xinjiang University, Urumqi, China

https://orcid.org/0009-0005-5413-691X

⨯

Res2Net-based multi-scale and multi-attention model for traffic scene image classification

Guanghui Gao,
Yining Guo,
Lumei Zhou,
Li Li,
Gang Shi

x

Published: May 20, 2024
https://doi.org/10.1371/journal.pone.0300017

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Traffic safety
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Deep learning
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Road traffic collisions
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