YOLOv8在疲劳驾驶检测中表现最佳，兼顾精度与实时性。
Cutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Models
- 采用YOLO系列模型对比检测疲劳行为
- YOLOv8在准确率与速度上均优于其他版本
- 适合智能驾驶安全系统开发人员参考
本研究基于现代目标检测算法，特别是YOLO系列模型（YOLOv5、YOLOv6、YOLOv7、YOLOv8），构建疲劳驾驶检测系统。通过对比各模型在真实场景下的表现，评估其对驾驶员疲劳行为的实时检测能力。针对环境变化多样性和检测精度不足等挑战，提出优化路径。实验表明，YOLOv8在准确性与推理速度之间取得最佳平衡，数据增强和模型优化显著提升系统在复杂驾驶条件下的适应性。
原文摘要 · Abstract (English)
This research delves into the development of a fatigue detection system based on modern object detection algorithms, particularly YOLO (You Only Look Once) models, including YOLOv5, YOLOv6, YOLOv7, and YOLOv8. By comparing the performance of these models, we evaluate their effectiveness in real-time detection of fatigue-related behavior in drivers. The study addresses challenges like environmental variability and detection accuracy and suggests a roadmap for enhancing real-time detection. Experimental results demonstrate that YOLOv8 offers superior performance, balancing accuracy with speed. Data augmentation techniques and model optimization have been key in enhancing system adaptability to various driving conditions.
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