计算机应用与软件2024，Vol.41Issue(6)：156-160,168,6.DOI:10.3969/j.issn.1000-386x.2024.06.023
基于卷积神经网络的疲劳检测改进算法
AN IMPROVED ALGORITHM FOR FATIGUE DETECTION BASED ON CNN
摘要
Abstract
In order to solve the shortcomings of low accuracy or poor real-time performance of current fatigue detection algorithms,an improved convolution neural network fatigue detection algorithm is proposed.HOG detection algorithm combined with KCF tracking algorithm was used to detect and track the collected faces.The Dlib library was called to extract the key points of the face.A deformable convolution neural network was introduced to identify the extracted eye and mouth states.This algorithm was tested by CEW and YAWDD data set.The accuracy of fatigue detection reaches 94.36％.Experiments show that compared with the current fatigue detection algorithms,the proposed method can detect driver fatigue in real time with high accuracy.关键词
人脸检测/Dlib/可变形卷积/状态识别/疲劳检测Key words
Face detection/Dlib/Deformable convolution/State recognition/Fatigue detection分类
信息技术与安全科学引用本文复制引用
周先春,邹清宇,陆滇..基于卷积神经网络的疲劳检测改进算法[J].计算机应用与软件,2024,41(6):156-160,168,6.基金项目
国家自然科学基金项目(11202106,61302188) （11202106,61302188）
江苏省大学生创新创业训练计划项目(202010300128P). （202010300128P）
