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农业机械操作员监控系统 (Ag-OMS):用于实时操作员安全评估的机器学习方法

Terence Irumva¹, Herve Mwunguzi¹, Santosh Kumar Pitla¹

¹Biological Systems Engineering, University of Nebraska, Lincoln, Nebraska, USA.

Journal of agricultural safety and health

|March 13, 2025

概括

一个新的农业机械操作员监控系统 (Ag-OMS) 使用机器学习实时分类拖拉机操作员安全. 该系统为不安全行为提供视觉和声音警报,改善农业安全.

科学领域:

农业安全工程农业安全工程
机器学习应用机器学习应用
人与计算机的交互

背景情况:

与拖拉机相关的伤害占农业生产者伤害的19%.
目前的安全监测方法往往是低效的,缺乏实时反.
农业机械操作人员需要自动化,实时安全行为监控系统.

研究的目的:

开发一种用于实时监测拖拉机操作员安全行为的自动化系统.
实施机器学习模型来分类安全行为.
整合一个反系统,对不安全的做法立即发出警报.

主要方法:

利用OpenPose,一个骨架检测算法,从机内摄像头料中实时检测姿势.
在拖拉机操作员安全行为 (低,中,高风险) 的三个类别上训练了一种机器学习模型.
开发了一种基于机载微控制器的反系统,用于实时预警.

主要成果:

农业机械操作员监测系统 (Ag-OMS) 成功开发.
该系统在识别所有安全风险类别的入口/出口操作员行为方面实现了97%的准确性.
实现了对操作员行为实时分类到低,中,高风险安全级别.

结论:

开发的Ag-OMS提供了一种有效的自动化解决方案,用于实时监控农业机械操作员安全.
系统对行为进行分类并提供即时反的能力提高了操作员的安全.
这项技术有可能在农业中显著减少与拖拉机相关的伤害.

关键词:

农业 - 世卫组织机器学习 (ML) 是指机器学习.打开Pose,可以使用Pose.安全行为安全行为.拖拉机操作员的拖拉机操作员

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