MIDAS-An Intelligent Mask Detection System

A Face Detection application using OpenCV, Deep Learning, and Python.The detection of masks using detection software by Machine Learning helps the public be more aware of their responsibility.

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Inspiration

Coronavirus disease 2019 has affected the world seriously. One major protection method for people is to wear masks in public areas. Many public service providers require customers to use the service only if they wear masks correctly.

What it does

Our software helps to focus on that aspect of safety and a route to the advanced technologies in the field of Machine Learning during the time of such rare pandemics where the public is divided and confused.

How we built it

Our face mask detector didn't use any morphed masked images dataset. The model is accurate, and since we used the MobileNetV2 architecture, it’s also computationally efficient and thus making it easier to deploy the model to embedded systems (Raspberry Pi, Google Coral, etc.). We need to break our project into 2 distinct phases, each with its own respective sub-steps: Training: Here it consists of loading our face mask detection dataset from disk, training a model (using Keras/TensorFlow) on this dataset, and then serializing the face mask detector to disk. Deployment: Once the face mask detector is trained, we can then move on to loading the mask detector, performing face detection, and then classifying each face as with_mask or without_mask.

This system can therefore be used in real-time applications that 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.

Challenges we ran into

Training the model to accurately classify with and without mask pictures was a new thing I tried, so it was kinda challenging.

Accomplishments that we're proud of

This system can therefore be used in real-time applications that require face-mask detection for safety purposes due to the outbreak of Covid-19. 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. Our face mask detector didn't use any morphed masked images dataset.

What we learned

I learned about, MobileNetV2, which improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. It also describes efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite.

What's next for MIDAS-An Intelligent Mask Detection System

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. Our face mask detector didn't use any morphed masked images dataset.

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