Mask Detection Using Deep Learning: A Step Towards Safer Environments
Introduction
In the wake of global pandemics, ensuring public safety through mask-wearing has become crucial. With the power of Deep Learning and Computer Vision, we can automate mask detection using Neural Networks and Convolutional Neural Networks (CNNs).
This project detects whether a person is wearing a mask or not in real-time using a Deep Learning model trained with CNN, achieving 99% accuracy. The model classifies images into three categories:
- With Mask
- Without Mask
Technologies Used
- Python
- TensorFlow & Keras (for Deep Learning)
- OpenCV (for real-time face detection)
- CNN (Convolutional Neural Networks)
- Flask (for deployment)
Model Training Process
1. Dataset Preparation
Used a dataset containing images of people with and without masks. Applied data augmentation to increase dataset diversity.
2. Model Architecture
The model follows a CNN-based architecture with layers including:
- Convolutional Layers (for feature extraction)
- MaxPooling Layers (for down-sampling)
- Flatten & Dense Layers (for classification)
- Dropout (to prevent overfitting)
Activation functions: ReLU for hidden layers, Softmax for classification.
3. Training and Accuracy
Model trained with Adam optimizer and Categorical Cross-Entropy loss, achieving an impressive 99% accuracy on the test set.
Deployment with Flask
Created a Flask web app to deploy the model. Integrated OpenCV for real-time video stream processing. The app captures live video from the webcam and classifies faces on the screen.
Results & Future Improvements
- The model successfully detects masks in real-time with high accuracy.
- Future improvements include mobile app integration and edge computing for faster processing.
Conclusion
This Mask Detection project demonstrates how Deep Learning can be used for real-time safety enforcement. By leveraging CNNs and Flask, we can deploy an intelligent system that helps ensure public health and safety.
Would love to hear your thoughts! Feel free to check out the code and contribute to further improvements. 🚀
You can check out the full project on my GitHub or view a live demo below:
View Demo