In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face dete...In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.展开更多
针对疲劳驾驶检测问题,提出了一种改进YOLOv5模型的人脸疲劳检测方法。首先,对YOLOv5模型增加检测层和添加CA注意力机制的改进,用于检测驾驶员的面部区域。其次,使用Dlib库中的级联回归算法实现人脸部68个特征点的标定和眼部、嘴部的定...针对疲劳驾驶检测问题,提出了一种改进YOLOv5模型的人脸疲劳检测方法。首先,对YOLOv5模型增加检测层和添加CA注意力机制的改进,用于检测驾驶员的面部区域。其次,使用Dlib库中的级联回归算法实现人脸部68个特征点的标定和眼部、嘴部的定位。最后,计算驾驶员眼部(EAR)和嘴部(MAR)的纵横比,依据眼睑闭合程度百分比(Percentage of Eyelid Closure Over the Pupil Over Time,PERCLOS)法则进行疲劳判定并进行预警处理。实验结果表明,改进后的YOLOv5算法的平均准确率达到92.5%,能够满足人脸疲劳检测对精度和速度的综合要求。展开更多
文摘In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.
文摘针对疲劳驾驶检测问题,提出了一种改进YOLOv5模型的人脸疲劳检测方法。首先,对YOLOv5模型增加检测层和添加CA注意力机制的改进,用于检测驾驶员的面部区域。其次,使用Dlib库中的级联回归算法实现人脸部68个特征点的标定和眼部、嘴部的定位。最后,计算驾驶员眼部(EAR)和嘴部(MAR)的纵横比,依据眼睑闭合程度百分比(Percentage of Eyelid Closure Over the Pupil Over Time,PERCLOS)法则进行疲劳判定并进行预警处理。实验结果表明,改进后的YOLOv5算法的平均准确率达到92.5%,能够满足人脸疲劳检测对精度和速度的综合要求。
