Lightweight and Real-Time Driver Fatigue Detection Based on MG-YOLOv8 with Facial Multi-Feature Fusion
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View abstract on PubMed
Summary
This summary is machine-generated.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.
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.
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