Research on Driver Facial Fatigue Detection Based on Yolov8 Model

TL;DR

This paper explores the application of the YOLOv8 deep learning model for driver fatigue detection, aiming to improve traffic safety by preventing fatigue-related accidents.

Contribution

It systematically analyzes the methods and algorithms of YOLOv8 for fatigue detection, offering a comprehensive technical solution for traffic safety enhancement.

Findings

YOLOv8 effectively detects driver fatigue in various datasets.

The study provides a detailed overview of current research and algorithms.

Proposes a robust framework for fatigue driving prevention.

Abstract

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.