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

The growing diversity of digital face manipulation techniques has led to an
urgent need for a universal and robust detection technology to mitigate the
risks posed by malicious forgeries. We present a blended-based detection
approach that has robust applicability to unseen datasets. It combines a method
for generating synthetic training samples, i.e., reconstructed blended images,
that incorporate potential deepfake generator artifacts and a detection model,
a multi-scale feature reconstruction network, for capturing the generic
boundary artifacts and noise distribution anomalies brought about by digital
face manipulations. Experiments demonstrated that this approach results in
better performance in both cross-manipulation detection and cross-dataset
detection on unseen data.

通过生成合成的混合图像并利用多尺度特征重建网络，实现更好的跨操作检测和跨数据集检测性能。