LiteNet: A Novel Approach for Traffic Sign Classification Using a Light Architecture
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-33-6893-4_4
Reference21 articles.
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5. Zhu Z, Liang D, Zhang S, Huang X, Li B, Hu S. (2016) Traffic-sign detection and classification in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2110–2118
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Reducing Deep Learning Complexity Toward a Fast and Efficient Classification of Traffic Signs;Lecture Notes on Data Engineering and Communications Technologies;2023
2. Towards Enhancing Traffic Sign Recognition through Sliding Windows;Sensors;2022-03-31
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