Advancing Road Safety: A CNN-Based Semi-Supervised Learning Approach for Drivers' Drowsiness Detection

Abstract

The National Safety Council (NSC) has reported that each year, driver fatigue is responsible for 100,000 accidents, 71,000 injuries, and 1,550 fatalities, often manifesting as drowsiness. Most current systems fail to anticipate accidents beforehand, focusing primarily on external factors. This paper introduces a novel drowsiness detection framework aimed at reducing accidents caused by drivers dozing off behind the wheel and minimizing harm to individuals engaged in prolonged computer use. The proposed method leverages a Convolutional Neural Network (CNN) with a semi-supervised learning technique, setting it apart from existing approaches. The effectiveness of our approach is evaluated using the UTA-RLDD video dataset, and the results demonstrate that the proposed method outperforms state-of-the-art methods, achieving 99.98% accuracy.

Salah Eddine Bekhouche

Former PhD Student

My research focuses on applied computer vision, pattern recognition, machine learning, and deep learning with a deep interest in biometrics, facial analysis, document understanding, and image/video generation.

Fadi Dornaika

Ikerbasque Research Professor

Ikerbasque Research Professor with expertise in computer vision, machine learning, and pattern recognition.