Partially Connected Neural Networks for an Efficient Classification of Traffic Signs
Road signs recognition plays an important role in improving traffic safety for both drivers and pedestrians. To ensure this recognition, many approaches are proposed by researchers. To overcome the limitations of the existing methods, Deep Learning approaches are used. This type of approaches achiev...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
FRUCT
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/4655cb2fc7214567a7f56846929a716e |
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| Sumario: | Road signs recognition plays an important role in improving traffic safety for both drivers and pedestrians. To ensure this recognition, many approaches are proposed by researchers. To overcome the limitations of the existing methods, Deep Learning approaches are used. This type of approaches achieves high recognition performances, and is also less sensitive to real world adverse conditions. However, they are in contrast very computationally expensive. From this perspective, the objective of this work is to adopt an approach that aims to reduce the computational complexity of these networks, in order to ensure a fast and efficient classification of traffic signs, especially for low and limited resources environments. |
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