A Novel Hybrid Approach for Driver Drowsiness Detection Using a Custom Deep Learning Model

Abstract

Driver Drowsiness Detection (D3) is a challenging task as it requires analysis based on various behavioral and physiological signs such as health issues, mental stress, and exhaustion. Data analytics reveals that driver drowsiness is the reason for one-fifth of all traffic accidents worldwide. Thus, safety devices are valuable for alerting sleepy drivers regarding more danger that may occur. Constant real-time drowsiness detection in complex conditions and denoting is still an open issue. However, facing these challenges, this article proposed a technique called Driver Drowsiness Detection using Custom Deep Learning Model (D3-CDLM). This approach contains four different modules: In the given procedure the investigation and exploration include, 1) feature extraction and selection; 2) machine learning and ensemble methods; 3) deep learning; and 4) the combination of the two, Hybrid. The first operation computes HOG, which stands for Histogram of Oriented Gradient, which is rotation and illumination invariant and resistant to the information in the local areas. Then Principal Component Analysis or PCA is used to obtain the best or top HOG features that are used as inputs to machine learning and ensemble methods-based modules. For hard to learn facial features, transfer learning is also carried out, and a new 30-layer CNNs structure is proposed called CDLM. Finally, the hybrid module’s top features are investigated using the PCA control of the architecture in coordination with the proposed CDLM for detecting drowsiness. Empirical analysis that encompasses all districts were applied on Yawning Detection Dataset. The results reveal that the developed and designed deep learning and hybrid modules acquire better accuracy than the proposed and utilized machine learning-based module along with the compared existing approaches in the pertinent literature.

Keywords

• Unusual behavior
• drowsiness detection
• HOG features
• PCA
• deep CNN