| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560656 |
| ISSN: | 1405-5546 |
| Autores: | Bekka, Reda1 Kherbouche, Samia1 Bouhissi, Houda El1 |
| Instituciones: | 1University of Bejaia, Faculty of Exact Sciences, Argelia |
| Año: | 2022 |
| Periodo: | Ene-Mar |
| Volumen: | 26 |
| Número: | 1 |
| Paginación: | 373-387 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | The Road safety is a major issue, both in terms of the number of road casualties and the economic cost of these accidents at the global, regional and national levels. Combating road insecurity is a priority concern for every country, as travel continues to increase and, despite the measures taken in many countries to improve road safety, much remains to be done in order to reduce the number of deaths and fatalities. In this paper, we review the most applied approaches in the detection of driver distractions. Furthermore, we propose a novel approach for preventing road crashes in the context of intelligent transport. Preliminary results indicate that the proposed methodology is efficient and provides high accuracy. |
| Keyword: | CNN, Distraction, Detection, Deep learning, Drowsiness, Intelligent transport, OpenCV, Transfer learning, VGG16 |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
