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Around 40 percent of accidents related to driving on highways in India occurdue to the driver falling asleep behind the steering wheel. Several types ofresearch are ongoing to detect driver drowsiness but they suffer from thecomplexity and cost of the models. In this paper, SleepyWheels a revolutionarymethod that uses a lightweight neural network in conjunction with faciallandmark identification is proposed to identify driver fatigue in real time.SleepyWheels is successful in a wide range of test scenarios, including thelack of facial characteristics while covering the eye or mouth, the driversvarying skin tones, camera placements, and observational angles. It can workwell when emulated to real time systems. SleepyWheels utilized EfficientNetV2and a facial landmark detector for identifying drowsiness detection. The modelis trained on a specially created dataset on driver sleepiness and it achievesan accuracy of 97 percent. The model is lightweight hence it can be furtherdeployed as a mobile application for various platforms.

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