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A Face Mask detection system based You Only Look Once (YOLO) architecture deploy in-browser with Serverless Edge Computing for COVID-19

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Face mask detector

Docker build

Visualize in WB

Table of contents

Abstract

Today is April 25, 2021, COVID-19 has affected countries all over the world. Turning back a few day ago, India has recorded approximately 2000 death case per day, which once again alert us about how dangerous this disease are.

In this project, our main purpose is to build a detection system, that able to detect a person is either wearing a mask. The system based on some YOLO [1] version for object detection, they are:

  • YOLOv3
  • YOLOv3 fastest
  • YOLOv5

By using the pre-defined models that were provided and supported by ultralytics. We will compare the result between these models, and implement a simple web application for run these model.

Training Result

Model size
(pixels)
mAPval
0.5:0.95
mAPval
0.5
mAPtest
0.5:0.95
mAPtest
0.5
Speed
V100 (ms)
params
(M)
YOLOv5s 640 65.4 93.1 65.4 93.2 6.3 7.3
YOLOv5m 640 66.5 93.9 66.7 93.7 7.9 21.4
YOLOv5l 640 65.8 93.9 66.9 93.8 12.1 47.0
YOLOv5x 640 66.5 93.5 67.3 94.0 20.7 87.7
YOLOv3 fastest 640 - - - - -
YOLOv3-tiny 640 55.7 87.9 55.6 87.8 3.4 8.8
YOLOv3-SSP 640 - - - - - 63.0
YOLOv3 640 - - - - - 61.9

We public our training result in wandb for if you want to dig deeper inside each model's training process, then make sure check out our project in W&B.

Dataset

The dataset is composed of WIDER Face [2] and MAFA [3]. WIDER Face dataset contains 32,203 images with 393,703 normal faces which are refered as non masked face, MAFA contains 30,811 images with 35,806 masked faces.

Dataset

Due to the limition of computational power, we using the dataset composed by AIZOOTech which only contains 7959 images in total, have been splited the dataset into 3 part: Train, Valid and Test; and converted them into YOLO format. You can find our dataset in Kaggle

Or by running Kaggle API:

kaggle datasets download -d nguyenmanhdung/facemaskyolo

Deployment

We've implemented a simple Flask application for demonstrate our work where located in /deployment folder.

The quick demo is in the figure below, where we can see the yolov3 tiny model have a acceptable accuracy and a ablity of detecting multiple faces.

Result

However, it's noticable some error that the model's made in some common scenarios in the video demonstration at Youtube:

Video demo

To run the Flask application, direct to the \deployment folder, install all the requirements and run the following command:

$ pip install -r requirements.txt

$ python app.py

The output will be like shown below, thus, access to http://127.0.0.1:5000/ (or the port you have configured) to open the application(the browser should as for Webcam permission).

 * Serving Flask app "app" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: on
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
 * Restarting with stat
 * Debugger is active!
 * Debugger PIN: 219-729-123

You might notice that we also support an builed application through Dockerfile, which you can find at Docker build

Note: We still in process of developing this deployment, if you have anytrouble, feel free to contact us.

Team member

Dung Manh Nguyen (me)

Hai Phuc Nguyen

Hoang Huy Nguyen

Reference

[1] Joseph Redmon et al.You Only Look Once: Unified, Real-Time Object Detection. 2016.arXiv:1506.02640 [cs.CV].

[2] Shuo Yang et al. “WIDER FACE: A Face Detection Benchmark”. In:IEEE Conference onComputer Vision and Pattern Recognition (CVPR). 2016.

[3] Adnane Cabani et al. “MaskedFace-Net–A dataset of correctly/incorrectly masked faceimages in the context of COVID-19”. In:Smart Health19 (2021), p. 100144.

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A Face Mask detection system based You Only Look Once (YOLO) architecture deploy in-browser with Serverless Edge Computing for COVID-19

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