Healthcare Technology

B N M Institute of Technology

Raspberry Pi based SOP Monitoring during COVID-19


The novel coronavirus (COVID-19) has raised world concern since it emerged in Wuhan, China in December 2019. It rapidly spread, resulting in an epidemic throughout China, followed by an increasing number of cases in other countries throughout the world. This project makes use of face mask detection system to make sure if a person is wearing a mask. Another feature of this project is to make sure social distancing in a public place.
This is achieved by video tracking. The first step we use object tracking to find the number of people present in a place, by allowing limited people maintaining social distance is easier. The second step is to make sure the people inside are maintaining a safe distance, this is done by checking if the distance between people is six feet (about two arms’ length). This project uses Tiny-YOLO algorithm (You Only Look Once) on raspberry pi for real-time object detection.
The YOLO object detector is often cited as being one of the fastest deep learning-based object detectors, achieving a high Fame Per Second (FPS), but YOLO is still not fast enough to run on embedded devices such as the Raspberry Pi. So, in this project makes use of tiny YOLO which has a small model size (< 50MB) and fast inference speed, making it suited for embedded deep learning devices such as the Raspberry Pi, Google Coral, and NVIDIA Jetson Nano. Overall, this project helps to reduce the number of COVID-19 patients and makes public places safer.

Project Team

Vinay Kumar J, Samarth M H, Shivaswaroop J P, Yukthi V
Electronics and Communication Engineering


Mrs. Anuradha J P
B N M Institute of Technology
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