In a society where traffic accidents frequently occur, fatigue driving has
emerged as a grave issue. Fatigue driving detection technology, especially
those based on the YOLOv8 deep learning model, has seen extensive research and
application as an effective preventive measure. This paper discusses in depth
the methods and technologies utilized in the YOLOv8 model to detect driver
fatigue, elaborates on the current research status both domestically and
internationally, and systematically introduces the processing methods and
algorithm principles for various datasets. This study aims to provide a robust
technical solution for preventing and detecting fatigue driving, thereby
contributing significantly to reducing traffic accidents and safeguarding
lives.

疲劳驾驶检测技术基于 YOLOv8 深度学习模型的研究与应用，以有效预防为目标，通过详细讨论模型方法和技术，介绍不同数据集的处理方法和算法原理，致力于提供强大的技术解决方案，减少交通事故和保护生命。

基于 Yolov8 模型的驾驶员面部疲劳检测研究

Research on Driver Facial Fatigue Detection Based on Yolov8 Model

Changes and advances in information technology have played an important role
in the development of intelligent vehicle systems in recent years. Driver
fatigue and distracted driving are important factors in traffic accidents.
Thus, onboard monitoring of driving behavior has become a crucial component of
advanced driver assistance systems for intelligent vehicles. In this article,
we present techniques for simultaneously detecting fatigue and distracted
driving behaviors using vision-based and machine learning-based approaches. In
driving fatigue detection, we use facial alignment networks to identify facial
feature points in the images, and calculate the distance of the facial feature
points to detect the opening and closing of the eyes and mouth. Furthermore, we
use a convolutional neural network (CNN) based on the MobileNet architecture to
identify various distracted driving behaviors. Experiments are performed on a
PC based setup with a webcam and results are demonstrated using public datasets
as well as custom datasets created for training and testing. Compared to
previous approaches, we build our own datasets and provide better results in
terms of accuracy and computation time.

通过使用基于视觉和基于机器学习的方法，同时检测疲劳和注意力分散的行为，以提高智能车辆系统的驾驶行为监测能力并获得更准确和时间更短的结果。

使用机器学习改进驾驶员疲劳和注意力分散的自动检测

Improving automatic detection of driver fatigue and distraction using  machine learning

Around 40 percent of accidents related to driving on highways in India occur
due to the driver falling asleep behind the steering wheel. Several types of
research are ongoing to detect driver drowsiness but they suffer from the
complexity and cost of the models. In this paper, SleepyWheels a revolutionary
method that uses a lightweight neural network in conjunction with facial
landmark identification is proposed to identify driver fatigue in real time.
SleepyWheels is successful in a wide range of test scenarios, including the
lack of facial characteristics while covering the eye or mouth, the drivers
varying skin tones, camera placements, and observational angles. It can work
well when emulated to real time systems. SleepyWheels utilized EfficientNetV2
and a facial landmark detector for identifying drowsiness detection. The model
is trained on a specially created dataset on driver sleepiness and it achieves
an accuracy of 97 percent. The model is lightweight hence it can be further
deployed as a mobile application for various platforms.

本文提出了一种名为 SleepyWheels 的革命性方法，该方法使用轻量级神经网络结合面部特征识别来实时识别司机疲劳。该模型采用 EfficientNetV2 和面部标记检测器进行训练，利用驾驶员疲劳数据集，实现了 97% 的准确性。