Abstract
The drowsiness of a person driving a vehicle is the primary cause of accidents all over the world. Due to lack of sleep and tiredness, fatigue and drowsiness are common among many drivers, which often leads to road accidents. Alerting the driver ahead of time is the best way to avoid road accidents caused by drowsiness. There are numerous techniques to detect drowsiness. In this paper, we have put forward a deep learning-based approach to detect the drowsiness of the drivers. We have used convolutional neural networks, which is a class of deep learning. We used the Face and Eye regions for detecting drowsiness. We have used the Closed Eye in the Wild dataset (CEW) and Yawing Detection Dataset (YawDD). We achieved an average accuracy of 96%.
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Rajkar, A., Kulkarni, N., Raut, A. (2022). Driver Drowsiness Detection Using Deep Learning. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_7
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DOI: https://doi.org/10.1007/978-981-16-2008-9_7
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