融合视觉与生理信号，提升疲劳检测准确率和实用性。
Dual-sensing driving detection model
- 结合面部特征与生理信号，通过多模态融合增强检测鲁棒性。
- 在真实驾驶场景中表现优异，准确率优于传统方法。
- 适合车载系统部署，兼顾性能与成本，具实际应用价值。
本文提出一种新型双模态驾驶员疲劳检测方法，融合计算机视觉与生理信号分析，利用两种传感模态的互补优势，突破单一模态方法的局限。系统设计创新架构，结合实时面部特征分析与生理信号处理，并采用先进融合策略，实现高效可靠的疲劳检测。该系统可在现有硬件上高效运行，同时保持高精度与可靠性。通过大量实验验证，本方法在受控环境与真实驾驶条件下均显著优于传统方法，且经多种驾驶场景测试，具备良好的实用性。研究为驾驶员疲劳检测提供了更可靠、低成本且人性化的新方案。
原文摘要 · Abstract (English)
In this paper, a novel dual-sensing driver fatigue detection method combining computer vision and physiological signal analysis is proposed. The system exploits the complementary advantages of the two sensing modalities and breaks through the limitations of existing single-modality methods. We introduce an innovative architecture that combines real-time facial feature analysis with physiological signal processing, combined with advanced fusion strategies, for robust fatigue detection. The system is designed to run efficiently on existing hardware while maintaining high accuracy and reliability. Through comprehensive experiments, we demonstrate that our method outperforms traditional methods in both controlled environments and real-world conditions, while maintaining high accuracy. The practical applicability of the system has been verified through extensive tests in various driving scenarios and shows great potential in reducing fatigue-related accidents. This study contributes to the field by providing a more reliable, cost-effective, and humane solution for driver fatigue detection.
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