基于MG-YOLOv8的轻量级和实时驾驶员疲劳检测,具有面部多功能融合
Chengming Chen1, Xinyue Liu1, Meng Zhou1
1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Journal of imaging
|November 26, 2025
概括
这项研究引入了一种改进的YOLOv8模型,用于检测驾驶员疲劳,提高道路安全. 该方法准确地识别了疲劳指标,为现实世界的应用提供了有前途的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路交通安全 道路交通安全
背景情况:
- 司机疲劳是交通事故的主要原因之一.
- 目前的疲劳检测方法缺乏准确性和效率.
研究的目的:
- 开发一种多功能融合疲劳检测方法,使用改进的YOLOv8模型.
- 为了提高驾驶员疲劳检测的准确性和计算效率.
主要方法:
- 使用了增强的YOLOv8来实现高精度的面部检测.
- 使用PFLD用于面部地标检测 (68分).
- 计算的疲劳指标:EAR,PERCLOS,MAR,HPR,BF,YF,NF使用一种新型的滑窗模型与EWMA和动态值.
主要成果:
- 在检测准确度和计算效率方面取得了显著的改进.
- 在实时性能和准确性之间实现了更好的平衡.
- 在WIDER FACE和YAWDD数据集上验证.
结论:
- 拟议的多功能融合方法有效地检测驾驶员的疲劳.
- 显示了现实世界驾驶安全应用的巨大潜力.
- 与现有方法相比,提供了更准确,更有效的解决方案.
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