STFTransNet:一个基于变压器的时空融合网络,用于增强的多模式驾驶员注意力缺失状态识别系统.
1Department of Artificial Intelligence Engineering, Chosun University, 309, Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一个新的AI模型,STFTransNet,用于识别驾驶员的注意力不集中. 它在面部部分遮蔽和照明不良等具有挑战性的条件下提高了准确性,提高了道路安全.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 司机的注意力不集中,包括昏昏欲睡和分心,是交通事故的主要原因.
- 现有的驾驶员注意力缺失识别系统面临诸如部分面部遮蔽和由于照明变化的图像退化等挑战.
研究的目的:
- 提出一种新的基于变压器的时空融合网络 (STFTransNet),用于强大的驾驶员注意力缺失状态识别.
- 为了提高驾驶员状态识别在现实驾驶场景中的准确性,隐蔽面部和不同的光线条件.
主要方法:
- 使用的介质管面网用于面部地标提取.
- 使用双流交叉注意力机制 (基于RCN) 来从面部和身体动作图像中学习空间特征.
- 实现了一个时间卷积网络 (基于TCN) 用于时间特征提取.
- 合并的空间和时间特征用于最终的驾驶员状态分类.
主要成果:
- 拟议的STFTransNet与多个公共数据集 (NTHU-DDD,StateFarm,YawDD) 上的现有模型相比,实现了更高的准确性.
- 在各自的数据集上,与基线模型相比,表现出4.56%,3.48%和3.78%的显著性能改善.
- 通过多模式融合有效地解决了部分面部遮蔽和光模糊引起的性能恶化.
结论:
- STFTransNet为先进的驾驶辅助系统提供了一个有前途的解决方案,通过精确的注意力缺失检测来提高安全性.
- 时空融合方法有效地应对现实世界驾驶条件的挑战,为更可靠的驾驶员监控铺平了道路.
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