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Personal protective equipment (PPE) is essential in many workplaces. Medical personnel wear PPE face masks to prevent the spread of viruses. However, sometimes staff do not comply with PPE regulations or do not wear masks correctly. To tackle this issue, Chinese researchers have developed a new type of automatic PPE monitoring technology.

A team from Northeast Electric Power University and Guangzhou University has created a machine learning system that can tell whether someone is wearing PPE. Although there are existing PPE detection systems, they suffer from background interference and find it difficult to accurately detect PPE in a wide range of body sizes. The Chinese team claims to have overcome these issues with two systems they call Criss-Cross Feature Pyramid Network and Deformable and Attention Residual with 50 layers. These modules, when combined, create an object detection network that works better than other PPE detection methods.
The system is expected to be tested on medical staff working in high-risk environments to ensure they are wearing the correct type of PPE masks that protect them and their patients.
This project is an example of how neural network technology is revolutionizing object detection through machine learning and artificial intelligence. The technology is continuously being developed to create more accurate systems.

Organizations need to issue quality PPE to all staff working in hazardous environments. Compliance monitoring systems are useful, but employees need training by health and safety experts on the importance of PPE equipment in the workplace and how to use it correctly.


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