一个面部表情感知边缘AI系统用于驾驶员安全监控
Maram A Almodhwahi1, Bin Wang1
1Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了一个深度学习的驾驶员监控系统 (DMS),以检测驾驶员的危险情绪状态. 先进的系统实现了高精度,通过实时识别潜在危险,提高了道路安全.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 道路安全工程 道路安全工程
背景情况:
- 道路安全是一个关键的全球性问题,由车辆所有权和交通量增加而加剧.
- 人类错误,包括分心和昏昏欲睡,是交通事故的主要原因.
- 当前的驾驶员监控系统 (DMS) 通常无法检测到关键的情绪和认知状态.
研究的目的:
- 开发一个强大的基于深度学习的DMS框架,用于实时检测和响应情绪驱动的驾驶员行为.
- 通过解决传统DMS的局限性来提高道路安全.
主要方法:
- 使用卷积神经网络 (CNN),包括Inception模块和基于Caffe的ResNet-10与单射击探测器 (SSD).
- 在一个包含各种情绪和现实世界驾驶场景的多样化数据集上训练DMS.
- 专注于高效和准确的面部检测和分类.
主要成果:
- 在检测情绪状态方面取得了98.6%的整体准确性.
- 在四种情绪状态中获得F1得分为0.979,精度为0.980,回忆力为0.979.
- 证明了计算效率和复杂性之间的平衡.
结论:
- 拟议的深度学习DMS为现实世界驾驶员监控提供了一种实用且高性能的解决方案.
- 能够精确地识别与驾驶相关的情绪,大大提高了预防事故的潜力.
- 在准确性和效率方面,与现有技术相比,代表了显著的进步.
关键词:
卷积神经网络是一种卷积神经网络.深度学习模型的深度学习模型驾驶员监控系统 驾驶员监控系统人类情绪和活动的识别识别.人机交互的人机交互车载监控 车载监控 车载监控实时监控实时监控道路交通安全问题 道路安全问题更多相关视频
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