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Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

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Lightweight and Real-Time Driver Fatigue Detection Based on MG-YOLOv8 with Facial Multi-Feature Fusion.

Chengming Chen¹, Xinyue Liu¹, Meng Zhou¹

¹College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.

Journal of Imaging

|November 26, 2025

Summary

This study introduces an improved YOLOv8 model for driver fatigue detection, enhancing road safety. The method accurately identifies fatigue indicators, offering a promising solution for real-world applications.

Keywords:

YOLOv8 face detection fatigue driving multi-feature fusion

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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Area of Science:

Computer Vision
Artificial Intelligence
Road Safety

Background:

Driver fatigue is a major cause of traffic accidents.
Current fatigue detection methods lack accuracy and efficiency.

Purpose of the Study:

To develop a multi-feature fusion fatigue detection method using an improved YOLOv8 model.
To enhance the accuracy and computational efficiency of driver fatigue detection.

Main Methods:

Utilized an enhanced YOLOv8 for high-precision face detection.
Employed PFLD for facial landmark detection (68 points).
Calculated fatigue indicators: EAR, PERCLOS, MAR, HPR, BF, YF, NF using a novel sliding window model with EWMA and dynamic thresholds.

Main Results:

Demonstrated significant improvements in detection accuracy and computational efficiency.
Achieved a better balance between real-time performance and accuracy.
Validated on WIDER FACE and YAWDD datasets.

Conclusions:

The proposed multi-feature fusion method effectively detects driver fatigue.
Shows strong potential for real-world driving safety applications.
Offers a more accurate and efficient solution compared to existing methods.