Vision-Based Driver Drowsiness Detection: From Deep Learning Models to Real-Time Mobile Deployment
Abstract
A significant percentage of traffic accidents worldwide result from sleepy drivers. Although detection methods have been established, their utility is often problematic. Physiological signals (EEG, ECG) and vision-based behavioral cues (eye closure, yawning) have been studied extensively, and deep learning models such as CNNs have shown excellent accuracy in controlled settings. Significant gaps still exist, however, especially regarding robustness against varying lighting and occlusions, on-road validation, and computationally efficient non-intrusive systems for real-time mobile deployment. This paper synthesizes and critiques current vision-based approaches and proposes a lightweight CNN architecture (MobileNetV2) optimized for ondevice inference with TensorFlow Lite, offering a scalable solution for road safety on common Android devices.
Fields of Study & Subjects
Sleep and Work-Related Fatigue
EEG and Brain-Computer Interfaces
Human-Automation Interaction and Safety
Publication details
- Journal / source
- International Journal for Research in Applied Science and Engineering Technology
- Publisher
- International Journal for Research in Applied Science and Engineering Technology
- Volume
- 14
- Issue
- 5
- Pages
- 163–171
- Publication date
- 7 May 2026