摘要
疲劳驾驶和不安全驾驶行为是引起交通事故的主要原因,随着智能交通技术的发展,利用深度学习算法进行驾驶行为检测已成为研究的热点之一。在卷积神经网络和长短时记忆神经网络的基础上,结合注意力机制改进网络结构,提出一种混合双流卷积神经网络算法,空间流通道采用卷积神经网络提取视频图像的空间特征值,以空间金字塔池化代替均值池化,统一了特征图的尺度变换,时间流通道采用SSD算法计算视频序列相邻两帧光流图像,用于人眼等脸部小目标的检测,再进行图像特征融合与分类,在LFW数据集和自建数据集中进行了实验,结果表明本方法的人脸识别和疲劳驾驶的检测准确率分别高于其他方法1.36和2.58个百分点以上。
Fatigue driving and unsafe driving behavior are the main causes of traffic accidents.With the progress of intelligent transportation technology,using deep learning algorithm to detect driving behavior has be⁃come one of the hotspots of research.On the basis of convolution neural network(CNN)and long⁃term memory neu⁃ral network,a hybrid dual stream convolution neural network algorithm is proposed by combining attention mecha⁃nism to improve network structure.In spatial flow channel,CNN is used to extract the spatial feature values of video image and the traditional mean pooling is replaced by spatial pyramid pooling is used to replace mean pooling,with the transformation of feature map unified.In time stream channel,single shot detection algorithm is adopted to cal⁃culate two adjacent frames of optical flow images of video sequence for detecting small facial targets such as human eyes.Then the fusion and classification of image features are carried out.Finally,experiments are performed on LFW dataset and self⁃built dataset.The results show that with the method adopted the accuracy of face recognition and fatigue driving detection is 1.36 and 2.5 percentage points higher than other methods respectively.
作者
施冬梅
肖锋
Shi Dongmei;Xiao Feng(Department of Computer Science and Technology,Suzhou College of Information Technology,Suzhou 215200;School of Computer Science and Engineering,Xi’an Technological University,Xi’an 710021)
出处
《汽车工程》
EI
CSCD
北大核心
2021年第8期1203-1209,1262,共8页
Automotive Engineering
基金
国家自然科学基金(61572394)
陕西省科技计划项目(2020GY-066)
江苏省自然科学基金(BK20191225)
2020年苏州高职高专第二批产学研合作基地项目(2020-5)资助。
