Resumo

BEKKA, Reda; KHERBOUCHE, Samia  e  BOUHISSI, Houda El. Distraction Detection to Predict Vehicle Crashes: a Deep Learning Approach. Comp. y Sist. [online]. 2022, vol.26, n.1, pp.373-387.  Epub 08-Ago-2022. ISSN 2007-9737.  https://doi.org/10.13053/cys-26-1-3871.

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.

Palavras-chave : CNN; distraction; detection; deep learning; drowsiness; intelligent transport; OpenCV; transfer learning; VGG16.

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