An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning

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.