# Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques

arXiv: https://arxiv.org/abs/1811.01627 · HTML: https://mlsanity.com/paper/1811.01627/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects](https://mlsanity.com/paper/2202.03274/) (2022)
- [A Survey on Drowsiness Detection -- Modern Applications and Methods](https://mlsanity.com/paper/2408.12990/) (2024)
- [Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset](https://mlsanity.com/paper/2111.00660/) (2021)
- [Machine vision detection to daily facial fatigue with a nonlocal 3D attention network](https://mlsanity.com/paper/2104.10420/) (2021)
- [Driving Safety Prediction and Safe Route Mapping Using In-vehicle and Roadside Data](https://mlsanity.com/paper/2209.05604/) (2022)
- [Driver Drowsiness Detection System: An Approach By Machine Learning Application](https://mlsanity.com/paper/2303.06310/) (2023)

## Abstract (arXiv)

Road crashes and related forms of accidents are a common cause of injury and death among the human population. According to 2015 data from the World Health Organization, road traffic injuries resulted in approximately 1.25 million deaths worldwide, i.e. approximately every 25 seconds an individual will experience a fatal crash. While the cost of traffic accidents in Europe is estimated at around 160 billion Euros, driver drowsiness accounts for approximately 100,000 accidents per year in the United States alone as reported by The American National Highway Traffic Safety Administration (NHTSA). In this paper, a novel approach towards real-time drowsiness detection is proposed. This approach is based on a deep learning method that can be implemented on Android applications with high accuracy. The main contribution of this work is the compression of heavy baseline model to a lightweight model. Moreover, minimal network structure is designed based on facial landmark key point detection to recognize whether the driver is drowsy. The proposed model is able to achieve an accuracy of more than 80%. Keywords: Driver Monitoring System; Drowsiness Detection; Deep Learning; Real-time Deep Neural Network; Android.