An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning11 Sep 2021 14:21
Abstract: Drowsy driving is one of the common causes of road accidents resulting in injuries, even
death, and significant economic losses to drivers, road users, families, and society. There have been
many studies carried out in an attempt to detect drowsiness for alert systems. However, a majority
of the studies focused on determining eyelid and mouth movements, which have revealed many
limitations for drowsiness detection. Besides, physiological measures-based studies may not be
feasible in practice because the measuring devices are often not available on vehicles and often
uncomfortable for drivers. In this research, we therefore propose two efficient methods with three
scenarios for doze alert systems. The former applies facial landmarks to detect blinks and yawns
based on appropriate thresholds for each driver. The latter uses deep learning techniques with
two adaptive deep neural networks based on MobileNet-V2 and ResNet-50V2. The second method
analyzes the videos and detects driver’s activities in every frame to learn all features automatically.
We leverage the advantage of the transfer learning technique to train the proposed networks on
our training dataset. This solves the problem of limited training datasets, provides fast training
time, and keeps the advantage of the deep neural networks. Experiments were conducted to test the
effectiveness of our methods compared with other methods. Empirical results demonstrate that the
proposed method using deep learning techniques can achieve a high accuracy of 97% . This study
provides meaningful solutions in practice to prevent unfortunate automobile accidents caused by
drowsiness.
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