Summary of Research on Driver Facial Fatigue Detection Based on Yolov8 Model, by Chang Zhou et al.
Research on Driver Facial Fatigue Detection Based on Yolov8 Model
by Chang Zhou, Yang Zhao, Shaobo Liu, Yi Zhao, Xingchen Li, Chiyu Cheng
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A paper on YOLOv8-based deep learning models for detecting driver fatigue proposes innovative methods and technologies to prevent traffic accidents. It explores the algorithms and dataset processing techniques used in YOLOv8, a state-of-the-art model for detecting fatigue driving, and discusses its current research status domestically and internationally. The study aims to develop a robust technical solution for preventing and detecting fatigue driving, ultimately reducing traffic accidents and saving lives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer models called YOLOv8 to detect when drivers are too tired to be on the road safely. Fatigue driving is a big problem that causes many car accidents. The paper explains how these models work, what kind of data they use, and how researchers around the world are working together to make sure these models are accurate and effective. The goal is to reduce traffic accidents and keep people safe by detecting fatigue driving before it’s too late. |
Keywords
* Artificial intelligence * Deep learning
