计算机应用与软件2024，Vol.41Issue(6)：115-122,8.DOI:10.3969/j.issn.1000-386x.2024.06.017
基于CNN和HOG的司机分心检测
DRIVER DISTRACTION DETECTION BASED ON CNN AND HOG
摘要
Abstract
Aimed at that the existing CNN network model only pays attention to the output of the last layer of the network without fully utilizing the features of the middle layer,which always contains much useful information,a driver distraction detection model is proposed,which extracts the output features of the multi-stage middle network layer end-to-end and integrates with HOG features.The parameter number of our model was only 3.6M.We used L2 weight regularization,Dropout,and batch regularization to improve model performance.The network was verified by the two public datasets State Farm Distracted Driver Detection(SFD3)and AUC Distracted Driver(AUCD2).The accuracy of SDF3 is 99.78％,which is about 3 percentage points higher than those existing methods,and the number of network parameters is reduced by about 95％.The accuracy of AUCD2 is 95.15％,which is about 2 percentage points higher than those existing methods,the number of network parameters is reduced by about 60％.关键词
分心检测/图像分类/HOG/CNNKey words
Distraction detection/Image classification/HOG/CNN分类
信息技术与安全科学引用本文复制引用
秦斌斌,钱江波,严迪群,董一鸿..基于CNN和HOG的司机分心检测[J].计算机应用与软件,2024,41(6):115-122,8.基金项目
宁波市自然科学基金项目(2019A610085) （2019A610085）
浙江省自然科学基金项目(LZ20F020001). （LZ20F020001）
