Face Mask Detection – Tesseract Technolabs

Case Study

project description

This was a system created for small to medium-scale stores where they can detect if the coming customers have worn face masks or not.
From the surveillance camera, we get the footage of the main door, and this image/video is sent to RassberyPi where the model is stored.
The model will predict whether the customers have masks on their faces or not and if anyone doesn’t have a mask then it will make an alarm sound at the door.
For the model, we have used the YOLOv5 model for training on the face mask dataset.
Moreover, it will also store information regarding how many people entered the store and the number of people who were not wearing masks in the database.

Technologies used

For model creation, we have used Python, and we have selected the YOLOv5 model for face mask detection, the reason its performance for object detection is considered as best.
For connecting the surveillance camera to the model we have used RasberryPi, which works as a small processor for computing the output from the model.
We have also used the OpenCV library available in python for image processing.

Difficulties we faced

The major difficulty we faced for this project was connecting the surveillance camera with the YOLOv5 model

Solutions

For this, we used RasberryPi for connecting the camera with the AI model and also for providing the necessary processing power to the model.

Computer Vision

4 months

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