Scholarlink

<p>The use of personal protective equipment (PPE) in industries is mandatory, since current regulations dictate the use of various types of PPE depending on the activity carried out. The correct use of these elements can be the difference between an incident and an accident with serious consequences for the people involved. In different industries, supervisors spend a large portion of their time ensuring that workers use their PPE correctly at all times. In order to help monitor worker safety, this work addresses the development of a prototype that allows the use of PPE to be detected automatically and send relevant notifications to supervisors. The prototype is developed in Python, and the detection of PPE is carried out through a deep neural network (YOLOv5). Preliminary inferences show that it can be adapted in a wide variety of scenarios with promising results.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Convolutional Neural Networks Mobile-App System to Detection of Personal Protection Elements in a Recycling Plant

  • 期刊论文

  • 发表日期：

    2025年8月8日

查看全文
  • Diego Alberto Godoy,
  • Enrique Marcelo Albornoz,
  • Carlos Kornuta,
  • Ricardo Selva,
  • Nicolas Ibarra,
  • Cesar Gallardo

摘要

The use of personal protective equipment (PPE) in industries is mandatory, since current regulations dictate the use of various types of PPE depending on the activity carried out. The correct use of these elements can be the difference between an incident and an accident with serious consequences for the people involved. In different industries, supervisors spend a large portion of their time ensuring that workers use their PPE correctly at all times. In order to help monitor worker safety, this work addresses the development of a prototype that allows the use of PPE to be detected automatically and send relevant notifications to supervisors. The prototype is developed in Python, and the detection of PPE is carried out through a deep neural network (YOLOv5). Preliminary inferences show that it can be adapted in a wide variety of scenarios with promising results.