基于姿态估计的粮仓作业人员摔倒检测研究
Falling Detection Study on Granary Workers Based on Human Pose Estimation
孙福艳 1桂崇文 1吕宗旺 1甄彤1
作者信息
- 1. 河南工业大学信息科学与工程学院,河南 郑州 450001
- 折叠
摘要
近些年时常发生粮仓作业人员因意外跌入粮堆无法自救、熏蒸作业中毒晕倒等安全问题.为了及时获取有效信息,减少对工人的伤害,提出一种自下而上的人体姿态估计方法,建立对粮仓工作人员状态检测模型.首先制作不同角度的粮仓作业视频和摔倒状态公共数据集;接着改进轻量级OpenPose算法,数据集引入算法生成骨骼图像;最后设计摔倒检测算法,得到一种基于姿态估计的粮仓作业人员摔倒检测模型.实验结果表明,检测摔倒状态准确率有所提高,轻量级OpenPose算法计算量较少,内存占用小,可以实时地检测粮仓作业人员摔倒状态.
Abstract
In recent years,there have been frequent safety issues such as the inability of grain warehouse workers to self rescue due to accidental falls into grain piles,and fainting due to poisoning during fumigation operations.In or-der to timely obtain effective information and reduce harm to workers,a bottom-up human pose estimation method is proposed,and a state detection model for granary workers is established.Firstly,we produced a public dataset of grain bin operation videos and falling states from different angles;Then we improved the lightweight OpenPose algorithm and introduced the dataset into the algorithm to generate skeletal images;Finally,we designed a falling detection al-gorithm to obtain a fall detection model for grain bin operators based on pose estimation.The experimental results show that the accuracy of detecting fall state is improved,and the lightweight OpenPose algorithm has lower computa-tional complixity and small memory occupation,and can detect the fall state of grain bin operators in real-time.
关键词
人体姿态估计/摔倒检测/轻量级算法Key words
Human pose estimation/Fall detection/Lightweight algorithm引用本文复制引用
基金项目
国家科技支撑计划(2018YFD0401404)
出版年
2024
NETL
NSTL
万方数据