Efficient Machine Learning-Based Drowsiness Detection for Enhanced Driving Safety: Real-Time Implementation

Authors

  • Pradeep Laxkar, Preetishree Patnaik, Samta Kathuria, Manish Tiwari, Nilesh Jain, Bal Krishna Sharma, Parth Gautam

Keywords:

Eye Aspect Ratio (EAR), Drowsiness, Drowsiness Detection System, Machine Learning, Mouth Aspect Ratio (MAR), Real Time.

Abstract

In today’s rapid time changing era, the count of road accidents is increasing day by day because of sleeping disorders and drowsiness. Technical enhancement in each and every area of day-to-day life, also demands the technically enhanced driving cars which detect drowsiness in driver with more accuracy and efficiency. This study presents a real-time drowsiness detection system for drivers, by blending the power of machine learning techniques to analyze facial features like Pupil of eye, EAR, MAR and NLR, considering the system (Car) watch, GPS system as well as utilizing the Advanced Driver Assistance Systems (ADAS) of smart cars. The system employs OpenCV and Dlib to extract eye, mouth aspect ratios and nose length ratio from video frames with the other gained feature of smart cars. The data undergoes standard scaling preprocessing before training a deep neural network for binary classification of drowsy and non-drowsy states. The model architecture comprises four dense layers with dropout and L2 regularization, ending in a softmax activation. Stratified K-Fold cross-validation is utilized for data splitting, and the model is compiled using the Adam optimizer and categorical cross-entropy loss, incorporating an early stopping callback to mitigate overfitting. The proposed system demonstrates exceptional performance, achieving more than 99% accuracy, 0.993 recall, and 0.991 F1 score in real-time drowsiness detection. These results hold potential for enhancing road safety and reducing fatigue-related accidents by accurately identifying drowsiness in drivers. With a capacity to detect drowsiness in real-time at a level of high accuracy, the proposed system has an immense potential to increase road safety and prevent accidents related to fatigue.

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References

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Published

26.03.2024

How to Cite

Pradeep Laxkar. (2024). Efficient Machine Learning-Based Drowsiness Detection for Enhanced Driving Safety: Real-Time Implementation. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1761–1771. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5747

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Section

Research Article