The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.
本申请公开了一种基于多维特征融合的疲劳驾驶监测预警方法,涉及智能交通与安全驾驶领域,该方法利用基于卷积神经网络的特征提取模型对采集到的车辆驾驶员的面部图像进行特征提取得到面部关键点,然后计算眼睛闭合率、嘴巴张开率、头部姿态数据以及面部肌肉数据最终得到多维特征后利用多层感知机进行融合得到融合面部特征,然后利用基于递归神经网络训练得到的疲劳状态检测模型对连续多个采样时刻的面部特征时间序列进行检测从而检测是否出现疲劳驾驶状态并触发报警反馈。该方法通过多维数据融合结合时序分析算法能够更准确的识别和判断驾驶员疲劳状态,提高监测预警准确性和可靠性。即使在光照变化或复杂驾驶环境下,系统也能保持高鲁棒性。
Fatigue driving monitoring and early warning method based on multi-dimensional feature fusion
一种基于多维特征融合的疲劳驾驶监测预警方法
2025-02-21
Patent
Electronic Resource
Chinese
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