Real time driver drowsiness detection using deep learning
Deep Learning, Drowsiness Detection, Object Detection, Mobile Nets, Single Shot Multibox Detector Classification
Driver drowsiness is a major cause of road accidents worldwide. This paper presents a real-time, non-intrusive drowsiness detection system using deep learning and computer vision. A front-facing camera captures driver facial features, and Convolutional Neural Networks (CNNs) are used to analyze eye state, blink rate, yawning, and head pose. The system classifies the driver’s condition as alert or drowsy and triggers an immediate warning. Experimental results on benchmark datasets show high accuracy and robustness under varying conditions. The proposed approach outperforms traditional methods and demonstrates strong potential for integration into Advanced Driver Assistance Systems (ADAS) to enhance road safety.
Registration ID: IJVRA_702440 Published ID: IJVRA2603880
"Real time driver drowsiness detection using deep learning ", IJVRA - International Journal of Versatile Research and Analysis (www.IJVRA.org), ISSN:2984-8903, Vol.4, Issue 3, page no.763-776, March-2026, Available :https://ijpub.org/ijvra/papers/IJVRA2603880.pdf
Paper Reg. ID: IJVRA_702440
Published Paper Id: IJVRA2603880
Research Area: Other area not in list
Country: Patan, Gujarat, India
ISSN: 2984-8903 | IMPACT FACTOR: 9.12 Calculated By Google Scholar | ESTD YEAR: 2023
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