The problem MaskD-Face mask Detection solves

In this Project, we have created a Face Mask Detector using Keras, Tensorflow, MobileNet and OpenCV. We have applied this on a Live Video Camera. With further improvements these types of models could be integrated with CCTV cameras to detect and identify people without masks.
The face mask detector didn't use any morphed masked images dataset. The model is accurate, and since the MobileNetV2 architecture is used, it is also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.).
This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed. This fast detection technique can help us to prevent the spread of Contagious Diseases.

Challenges we ran into

Data Shortage was the main problem. We were able to overcome this by employing data augmentation and ensemble techniques.