首页|基于改进YOLOv5的驾驶员分心驾驶检测

基于改进YOLOv5的驾驶员分心驾驶检测

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
针对采用分类方法进行分心驾驶检测存在只能识别有限分心驾驶行为类别以及忽视时间信息的问题，提出了基于改进YOLOv5的驾驶员分心驾驶检测方法。首先，在YOLOv5的基础上引入Ghost模块，采用线性变换代替部分常规卷积进行特征提取以轻量化网络模型，实现快速又准确地检测图像中手机、水杯、驾驶员双眼和头部区域;其次，在获取目标检测结果的基础上，结合头部姿态估计设计逻辑算法并融入YOLOv5中，从认知分心和视觉分心两个角度检测每帧图像中驾驶员是否存在分心驾驶，避免了分类方法受限分心驾驶类别数的问题，再设置适当的时间阈值，从而实现端到端实时的分心驾驶预警;最后，对采集的18名驾驶员的驾驶行为数据集进行对比试验，验证了本文方法的可行性和有效性。
Driver distracted driving detection based on improved YOLOv5
To address the problem that distracted driving detection using classification methods can only identify a limited number of distracted driving behavior categories and ignore temporal information,we propose a distracted driving detection method based on improved YOLOv5.First,the Ghost module is introduced on the basis of YOLOv5,and linear transformation is used instead of partial conventional convolution for feature extraction to lighten the network model to achieve fast and accurate detection of cell phone,water bottle,driver's eyes and head region in the image;second,after obtaining the target detection results,the logic algorithm is designed to detect the presence of distracted driving in each frame by combining with head pose estimation.Second,on the basis of obtaining the target detection results,a logic algorithm is designed and integrated into YOLOv5 with head pose estimation to detect the presence of distracted driving in each frame from both cognitive distraction and visual distraction perspectives,which avoids the problem that the classification method is limited by the number of distracted driving categories,and then setting an appropriate time threshold,thus realizing real-time and effective distracted driving detection;finally,three sets of experiments are conducted on the collected driving behavior dataset of 18 drivers to verify the feasibility and effectiveness of the method in this paper.

distracted drivingYOLOv5driving behaviortarget detectionhead pose estimation

陈仁祥、胡超超、胡小林、杨黎霞、张军、何家乐

展开 >

重庆交通大学 交通工程应用机器人重庆市工程实验室,重庆 400074

重庆工业大数据创新中心有限公司,重庆 400056

重庆科技学院 工商管理学院,重庆 401331

分心驾驶 YOLOv5 驾驶行为 目标检测 头部姿态估计

国家重点研发计划国家自然科学基金重庆市自然科学基金创新发展联合基金重庆市研究生联合培养基地项目重庆市教委科学技术研究计划重庆交通大学研究生科研创新项目

2018YFB130660151975079CSTB2023NSCQ-LZX0127JDLH-PYJD2021007KJZD-M2022007012022S0045

2024

10.13229/j.cnki.jdxbgxb.20220638

吉林大学学报(工学版)
吉林大学

吉林大学学报(工学版)

CSTPCD北大核心
影响因子：0.792
ISSN：1671-5497
年,卷(期)：2024.54(4)
  • 22