Developing a Deep Learning Facemask Detection Prototype in Two Days

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 facemasks, but if a facemask is 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 facemask detection dataset used 2 publicly available libraries with over 1000 images to provide examples of people with, without, and incorrectly wearing facemasks in different environments. Other cameras suited for this purpose include the Blackfly S GigE – for more information about FLIR machine vision solutions, contact sales.

AN ADAPTABLE SOLUTION
Each image in the facemask dataset was annotated with bounding boxes showing object locations and class labels indicating which faces had the mask on, which did not, and if they were worn appropriately. Deep learning developers and solution integrators can easily expand this solution to cover more complex and robust use cases for deployment in the real world. For example, the neural network can be trained to detect face shields, gowns, gloves, and other PPE within high risk / high traffic environments like hospitals and airports.

Learn more about building a deep learning solution on a budget, getting started with the Firefly DL, and FLIR temperature screening solutions.