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


  • 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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