整合轻量级YOLOv5s和面部3D关键点,用于增强疲劳驾驶检测
Mohan Arava1, Divya Meena Sundaram1
1SCOPE, VIT-AP University, Amaravathi, AP, India.
PeerJ. Computer science
|February 3, 2025
概括
这项研究引入了一个先进的AI系统,使用YOLOv5和3D面部地标来实时检测驾驶员的昏昏欲睡. 改进后的模型显著提高了识别疲劳的准确性,旨在防止因昏昏欲睡的驾驶造成的事故.
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:
- 司机疲劳是车辆事故的主要原因之一.
- 及时警告可以防止事故.
- 人工智能和计算机视觉为驾驶员监控提供了新的方法.
研究的目的:
- 开发一种全面的方法来检测驾驶员注意力状态.
- 通过识别和减轻昏昏欲睡,提高驾驶员的安全.
- 改进现有的实时疲劳检测方法.
主要方法:
- 使用了一种增强的你只看一次 (YOLOv5) 算法.
- 在YOLOv5.5中集成Swin变压器模块和精细的特征提取功能.
- 员工使用面部标志,并计算眼睛和口腔的比例,以检测疲劳线索.
- 集成的注意力网 3D关键点,用于实时检测.
主要成果:
- 与基线模型相比,平均平均精度 (mAP) 提高了2.4%.
- 与传统的疲劳驾驶检测模型相比,已经证明了更高的有效性.
- 通过YOLOv5和3D面部标志的结合方法,可以确保准确而强大的疲劳检测.
结论:
- 拟议的AI模型有效地使用先进的计算机视觉技术检测驾驶员的昏昏欲睡.
- 该系统为减少因驾驶员疲劳造成的事故提供了一个有前途的解决方案.
- 实时监控和警报系统对于提高道路安全至关重要.
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