针对疲劳驾驶检测问题，提出一种基于人脸图像特征的眼部疲劳检测方法。利用RetinaFace网络检测面部区域的位置;通过级联回归树(ERT，Ensemble of Regression Trees)算法获取人脸68个关键特征点，同时完成对眼部区域的划分;计算人眼纵横比，判断出睁眼和闭眼行为;根据PERCLOS度量准则实现疲劳状态的检测与判定。在YawDD数据集上的实验结果表明，该方法识别的平均准确率、精确率和召回率分别为90。24％、92。41％和91。90％，能有效识别眼部疲劳状态。
EYE FATIGUE DETECTION BASED ON RETINAFACE AND ERT
Aimed at the problem of fatigue driving detection,a method of eye fatigue detection based on facial image features is proposed.We used the RetinaFace network to detect the facial area.The Ensemble of Regression Trees(ERT)algorithm was used to obtain 68 key feature points,and the eye regions were divided.We calculated the eye aspect ratio to detect the blink behavior.According to the PERCLOS criterion,the detection and determination of the fatigue state was realized.The experimental results on the YawDD dataset show that the average accuracy,precision,and recall rate of this method are 90.24％,92.41％and 91.90％,which can effectively identify eye fatigue.
Eye fatigue detectionRetinaFace networkEnsemble of regression treesEye aspect ratio
万方数据
维普
