摘要
针对现有分心驾驶行为检测方法存在的检测精度低、实时性差等问题,利用基于深度学习的目标检测方法进行了驾驶员分心驾驶行为检测,首先构建分心驾驶行为数据集,包括驾驶员使用手机、饮水和吸烟3种行为的图像,并进行目标物的标注,然后选用轻量化目标检测模型NanoDet进行训练验证,结果表明,该方法可以准确并快速地识别出驾驶员在驾驶过程中使用手机、饮水和吸烟的行为。
To address some of the problems in existing distracted driving behavior detection methods,such as low detection accuracy and poor real-time performance,a deep learning-based target detection method was used for driver distracted driving behavior detection.Firstly,a distracted driving behavior dataset was constructed,including images of drivers using mobile phones,drinking water and smoking,and the targets were annotated,secondly a lightweight target detection model NanoDet was selected for training and validation.The results show that the method can accurately and quickly identify driver behaviors including using mobile phones,drinking water and smoking while driving.
作者
曹立波
杨洒
艾昌硕
颜京才
李旭升
Cao Libo;Yang Sa;Ai Changshuo;Yan Jingcai;Li Xusheng(State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha 410082;Baoding Branch of Haomo Technology Co.,Ltd.,Baoding 071000)
出处
《汽车技术》
CSCD
北大核心
2023年第6期49-54,共6页
Automobile Technology
关键词
分心驾驶
目标检测
数据集标注
轻量化模型
Distracted driving
Target detection
Dataset annotation
Lightweight model
