Developing a deep learning face mask detection prototype in two days
08 March 2021
Face masks are a critical tool for fighting the spread of COVID-19 and are proven to be most effective when face coverings are worn universally. As stores and businesses reopen, ensuring all occupants wear a face mask is essential.
However, the additional resources required to monitor patrons can further strain businesses that are already struggling to meet other sanitation and social distancing guidelines. Deep learning solutions are capable of automatically detecting anyone in violation of face mask guidelines, saving employee time and ensuring safer environments.
Deploying deep learning
Deep learning is a form of machine learning that uses neural networks with many ‘deep’ layers between the input and output nodes. By training a network on a large data set, a model is created that can be used to make accurate predictions based on unseen data. In this case, the network can be trained to detect not only the face masks themselves, but also whether they are worn correctly on a person´s face.
A fully functioning deep learning system can be developed and deployed in a matter of days. Using a FLIR Firefly DL camera, FLIR engineers developed a system for detecting compliance and flagging users who may be in violation of PPE (Personal Protection Equipment) guidelines. The face mask detection dataset used two publicly available libraries with over 1000 images to provide examples of people who were wearing masks, those who weren’t, and those who were wearing them incorrectly, in different environments. Blackfly S GigE is another camera that is suited for this purpose
Read the full article in the March issue of DPA.
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