This study presents a novel driver drowsiness detection system that combines
deep learning techniques with the OpenCV framework. The system utilises facial
landmarks extracted from the driver's face as input to Convolutional Neural
Networks trained to recognise drowsiness patterns. The integration of OpenCV
enables real-time video processing, making the system suitable for practical
implementation. Extensive experiments on a diverse dataset demonstrate high
accuracy, sensitivity, and specificity in detecting drowsiness. The proposed
system has the potential to enhance road safety by providing timely alerts to
prevent accidents caused by driver fatigue. This research contributes to
advancing real-time driver monitoring systems and has implications for
automotive safety and intelligent transportation systems. The successful
application of deep learning techniques in this context opens up new avenues
for future research in driver monitoring and vehicle safety. The implementation
code for the paper is available at
this https URL

本研究提出了一种新的司机瞌睡检测系统，结合了深度学习技术和 OpenCV 框架，利用从司机脸部提取的面部标志作为卷积神经网络的输入，训练模型识别瞌睡模式，并集成 OpenCV 实现实时视频处理，从而使该系统适用于实际应用。广泛的实验证明该系统在检测瞌睡方面具有高准确性、敏感性和特异性，能够通过及时提醒来防止由司机疲劳引起的事故，这对提高道路安全具有潜在的意义。该研究对于推进实时司机监控系统以及汽车安全和智能交通系统具有重要意义，而在这一背景下成功应用深度学习技术为未来司机监控和车辆安全的研究开辟了新的方向。