Real-Time Face Mask Detection using Convolutional Neural Networks and Deployment with Django Framework

Abstract

Real-time face mask detection is an essential application in the current scenario to ensure public safety. This project aims to develop a deep learning model using Convolutional Neural Networks (CNNs) to detect whether individuals are wearing face masks or not in a live video feed. The model will be deployed into a web application using Django Framework for the back-end and HTML5, CSS3, and Bootstrap for front-end development, providing a user-friendly interface for real-time monitoring.

1. Introduction

The COVID-19 pandemic has highlighted the importance of wearing face masks to mitigate the spread of the virus. To automate the process of monitoring mask compliance, we propose a real-time face mask detection system using CNNs. The web application will allow users to observe live video streams and receive immediate feedback on whether people in the video are wearing masks or not.

2. Data Collection and Preprocessing

A dataset containing images of individuals with and without face masks will be collected. This dataset will be preprocessed by resizing, normalization, and augmentation to enhance the model's performance.

3. Model Architecture

The face mask detection model will be built using Convolutional Neural Networks, a deep learning approach known for its effectiveness in image-related tasks. Transfer learning techniques can also be explored using pre-trained CNN models like VGG, ResNet, or MobileNet for faster convergence and improved accuracy.

4. Model Training

The preprocessed dataset will be split into training and validation sets. The CNN model will be trained on the training set, and its hyperparameters will be tuned to achieve optimal performance. The validation set will be used to prevent overfitting.

5. Real-Time Video Feed Integration

For real-time video feed processing, OpenCV will be utilized to capture the video stream from the user's camera. The captured frames will be fed into the trained CNN model to predict mask-wearing status.

6. Django Framework for Back-End

The back-end of the web application will be developed using Django Framework. It will handle video stream processing, model inference, and communicate with the front-end to display results.

7. Front-End Development

HTML5, CSS3, and Bootstrap will be used to create an intuitive and responsive user interface. The front-end will communicate with the Django back-end through API calls to receive real-time predictions.

8. Model Deployment

The trained face mask detection model will be serialized and deployed as part of the Django web application. This will allow users to access the application from their browsers without the need for additional installations.

9. Testing and Evaluation

The performance of the real-time face mask detection system will be evaluated using metrics like accuracy, precision, recall, and F1 score. It will be tested with various real-world scenarios to ensure its robustness.

10. Conclusion

Real-Time Face Mask Detection using Convolutional Neural Networks and deployed through Django Framework will provide an efficient and user-friendly solution for monitoring face mask compliance. This system can be used in public spaces, workplaces, and other areas where mask-wearing is essential to prevent the spread of infectious diseases.