LAUSR

Abstract This project demonstrates the application of Artificial Intelligence (AI) and machine vision for the identification of Personal Protective Equipment (PPE), particularly safety glasses in zones of the Learning Factory, where safety risks exist. The objective is to design and implement an automated system for ensuring the safety of personnel when they are in the vicinity of machinery that presents potential risks to the eyes. Microsoft Azure Custom Vision AI and Intelligent AI Services, in conjunction with low-cost vision devices with lightweight onboard AI capability, provide a platform for a deep learning neural network model using publicly available images under the Creative Commons License. A combination of cloud-based and on-premises AI is used in this proof of concept system to provide a real-time vision-based safety system capable of detecting and recording potential safety breaches, promoting compliance, and ultimately preventing accidents before they happen. This system can be used to initiate different control actions in the event of safety violations and can detect multiple forms of protective wear. The flexibility of the system offers multiple benefits to learning factories and manufacturing organizations such as improved user safety, reduced insurance costs, and better detection and recording of safety violations. The hybrid AI architecture approach allows for flexibility in training and deployment based on the capability of local computing resources.