基于CNN和HOG的司机分心检测
DRIVER DISTRACTION DETECTION BASED ON CNN AND HOG
秦斌斌 1钱江波 1严迪群 1董一鸿1
作者信息
- 1. 宁波大学信息科学与工程学院 浙江宁波 315211
- 折叠
摘要
针对现有CNN网络模型只关注网络最后一层的输出而未能充分利用中间层的输出特征,而事实上中间层特征包含很多有用信息,提出一种端到端的提取多阶段中间网络层输出特征,并与HOG(Histogram of Oriented Gradient)特征融合的司机分心检测模型.模型参数量仅为3.6 M,同时采用L2权重正则化、Dropout以及批量正则化对模型性能进行提升.在两个公开的数据集State Farm Distracted Driver Detection(SFD3)和AUC Distracted Driver(AUCD2)进行了实验验证,在SFD3准确度达到99.78％,比现有论文提高约3百分点,网络参数量分别减少约95％;在AUCD2上准确度达到95.15％,比现有论文提高约2百分点,网络参数量减少约60％.
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引用本文复制引用
基金项目
宁波市自然科学基金项目(2019A610085)
浙江省自然科学基金项目(LZ20F020001)
出版年
2024
国家科技期刊平台
NSTL
万方数据