基于面部多特征融合的疲劳驾驶检测系统设计OA
Design of Fatigue Driving Detection System Based on Facial Multi-feature Fusion
针对疲劳驾驶引发的交通事故频发现象,文章设计了一种基于多特征融合的疲劳驾驶检测系统.系统通过 Dlib 和 OpenCV 技术提取驾驶员眼部开合程度、嘴部动作单元及头部姿态角三类关键特征,结合机器学习算法构建疲劳状态分类模型.实验结果表明,该方法在自建数据集上的检测准确率达97.6％,有效提升了疲劳状态识别的鲁棒性.研究验证了多特征融合策略在驾驶行为监测中的应用价值,为降低道路交通事故风险提供了技术支撑.
To address the frequent traffic accidents caused by fatigue driving,this paper designs a fatigue driving detection system based on multi-feature fusion.The system utilizes Dlib and OpenCV to extract key features of drivers,including eye-opening degree,mouth action units,and head posture angles.These features are integrated with machine learning algorithms to construct a classification model for identifying fatigue states.Experimental results demonstrate that the method achieves an accuracy rate of 97.6％on a self-built dataset,significantly enhancing the robustness of fatigue detection.The research validates the application value of multi-feature fusion strategies in driving behavior monitoring and provides technical support for mitigating traffic accident risks.
武伟;李佳健;陈奕锦;王子荣;席梓晗
山西大同大学,山西 大同 037009昆明理工大学,云南 昆明 650500山西大同大学,山西 大同 037009山西大同大学,山西 大同 037009山西大同大学,山西 大同 037009
信息技术与安全科学
疲劳驾驶检测面部特征特征提取多特征融合
fatigue driving detectionfacial featurefeature extractionmulti-feature fusion
《现代信息科技》 2026 (11)
126-132,137,8
山西大同大学教学改革创新项目(XJG2023262)大同市科技局应用基础研究项目(2024080)山西省高等学校教学改革创新项目(J20241141)
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