Implementing a Real-time, AI-Based, Face Mask Detector Application for COVID-19
Face Mask Detector
Businesses are constantly overhauling their existing infrastructure and processes to be more efficient, safe, and usable for employees, customers, and the community. With the ongoing pandemic, it’s even more important to have advanced analytics apps and services in place to mitigate risk. For public safety and health, authorities are recommending the use of face masks and coverings to control the spread of COVID-19.
NVIDIA developed NVIDIA Clara Guardian, which is an application framework and partner ecosystem that simplifies the development and deployment of smart sensors with multimodal AI in healthcare facilities. Clara Guardian comes with a collection of healthcare-specific, pretrained models and reference applications that are powered by GPU-accelerated application frameworks, toolkits, and SDKs. You can use NVIDIA Transfer Learning Toolkit (TLT) to develop highly accurate, intelligent video analytics (IVA) models with zero coding and use the NVIDIA DeepStream SDK to deploy multi-platform scalable video analytics.
In this post, we show experiments using TLT to train a face mask detection model and then using the DeepStream SDK to perform efficient, real-time deployment of the trained model. Face mask detection systems are now increasingly important, especially in smart hospitals for effective patient care. They’re also important in stadiums, airports, warehouses, and other crowded spaces where foot traffic is heavy and safety regulations are critical to safeguarding everyone’s health.
This post only outlines the developer recipe. No trained model or datasets are provided by NVIDIA. You can access the recipe and scripts to build your own app using the NVIDIA-AI-IOT/face-mask-detection GitHub repo.