During the COVID-19 pandemic, face masks became a crucial tool in preventing the spread of the virus. To encourage their use, many organizations and businesses implemented face mask detection systems. This project aims to develop a real-time face mask detection system that can accurately identify individuals wearing or not wearing a face mask as they pass in front of a camera.
System Design:
Hardware: Camera: A high-resolution camera capable of capturing clear images in various lighting conditions. Processor: A powerful processor (e.g., a Raspberry Pi or a dedicated computer) to handle image processing and machine learning tasks. Display: An optional display to provide real-time feedback or alerts.
Software: Programming Language: Python, due to its extensive libraries for image processing and machine learning. Libraries: OpenCV: For image processing, face detection, and video capture. TensorFlow/Keras: For building and training the deep learning model. NumPy: For numerical computations.
Machine Learning Model: Dataset: A large dataset of images with and without face masks, ideally collected from diverse sources to ensure robustness. Model Architecture: A Convolutional Neural Network (CNN) is well-suited for image classification tasks.
Real-Time Detection:
Video Capture: The system continuously captures video frames from the camera. Face Detection: OpenCV is used to detect faces in each frame. Mask Detection: The detected faces are fed into the trained CNN, which predicts whether a mask is worn or not. Output: The system can provide various outputs: Displaying real-time alerts on a screen.