SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention

TL;DR

SleepyWheels introduces a lightweight neural network-based system that accurately detects driver drowsiness in real time, aiming to prevent highway accidents caused by falling asleep at the wheel.

Contribution

It presents a novel, efficient ensemble model combining facial landmark detection with a lightweight neural network for real-time drowsiness detection.

Findings

Achieved 97% accuracy on a custom driver sleepiness dataset.

Effective across diverse facial features, camera angles, and lighting conditions.

Suitable for deployment on mobile platforms due to its lightweight design.

Abstract

Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification is proposed to identify driver fatigue in real time. SleepyWheels is successful in a wide range of test scenarios, including the lack of facial characteristics while covering the eye or mouth, the drivers varying skin tones, camera placements, and observational angles. It can work well when emulated to real time systems. SleepyWheels utilized EfficientNetV2 and a facial landmark detector for identifying drowsiness detection. The model is trained on a specially created dataset on driver sleepiness and it achieves an accuracy of 97 percent. The model is lightweight hence it can be further deployed as a mobile application for various platforms.