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<p>Worker safety is a significant concern in the manufacturing industry, as it is in many other sectors. Workers who work in factories may be at danger from things like moving machinery, dangerous chemicals, and falling objects. Wearing safety gear is essential to shield employees from these dangers, but it performs best when worn appropriately. Several different devices, including laptops, smartphones, and many others, are compatible with the system. One of the many businesses that suffer with worker safety is the manufacturing industry. Workers who work in factories may be at danger from things like moving machinery, dangerous chemicals, and falling objects. Wearing safety gear is essential to shield employees from these dangers, but it performs best when worn appropriately. The technique described in this research uses YOLOv8, a cutting-edge deep learning object identification model, to find people wearing necessary safety gear. To illustrate the technique, images of employees wearing and not wearing safety gear were shown. The device can be installed in a workplace after training to monitor employees and provide them with real-time feedback to make sure they are wearing the appropriate safety gear. Our method offers a few benefits over previous safety device identification methods. It is scalable and can be used to track many employees at once. The YoloV8 technology has the potential to significantly improve workplace security. By requiring employees to wear the appropriate safety gear at all times, the strategy can help to lower injuries and fatalities. Helmets, vests, and other safety equipment will be immediately detected. The YOLOv8 deep learning model is used in the suggested method for recognising safety equipment. The effective YOLOv8 single-stage object identification model. It might be developed and applied to track multiple employees simultaneously. The model is trained using vests, shoes, and helmets worn and not worn by workers. Images from many sources, such as social media, the internet, and factory security cameras, were used to create the dataset. After training, real-time footage of industrial workers will be displayed to the model, and she will be asked to judge whether or not the worker followed safety measures.</p>

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Safety equipment detection using YOLOv8

  • 期刊论文

  • 发表日期：

    2025年9月5日

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  • M. Vijayalakshmi,
  • A. J. Roshan Jose,
  • G. K. Vishal,
  • N. Vishwanath

摘要

Worker safety is a significant concern in the manufacturing industry, as it is in many other sectors. Workers who work in factories may be at danger from things like moving machinery, dangerous chemicals, and falling objects. Wearing safety gear is essential to shield employees from these dangers, but it performs best when worn appropriately. Several different devices, including laptops, smartphones, and many others, are compatible with the system. One of the many businesses that suffer with worker safety is the manufacturing industry. Workers who work in factories may be at danger from things like moving machinery, dangerous chemicals, and falling objects. Wearing safety gear is essential to shield employees from these dangers, but it performs best when worn appropriately. The technique described in this research uses YOLOv8, a cutting-edge deep learning object identification model, to find people wearing necessary safety gear. To illustrate the technique, images of employees wearing and not wearing safety gear were shown. The device can be installed in a workplace after training to monitor employees and provide them with real-time feedback to make sure they are wearing the appropriate safety gear. Our method offers a few benefits over previous safety device identification methods. It is scalable and can be used to track many employees at once. The YoloV8 technology has the potential to significantly improve workplace security. By requiring employees to wear the appropriate safety gear at all times, the strategy can help to lower injuries and fatalities. Helmets, vests, and other safety equipment will be immediately detected. The YOLOv8 deep learning model is used in the suggested method for recognising safety equipment. The effective YOLOv8 single-stage object identification model. It might be developed and applied to track multiple employees simultaneously. The model is trained using vests, shoes, and helmets worn and not worn by workers. Images from many sources, such as social media, the internet, and factory security cameras, were used to create the dataset. After training, real-time footage of industrial workers will be displayed to the model, and she will be asked to judge whether or not the worker followed safety measures.