Detection of Face Masks in Real Time
International Research Journal of Modernization in Engineering Technology and Science · 2023
Real-time face mask detection has become a crucial task in ensuring public safety and adherence to maskwearing guidelines.In this project, we present a system for real-time face mask detection using Python.The system utilizes computer vision techniques and deep learning algorithms to accurately identify whether individuals in a video stream are wearing masks or not.Our approach involves leveraging the OpenCV library for real-time video processing and face detection.We employ a pre-trained convolutional neural network (CNN) model, such as MobileNetV2 or ResNet, for mask classification.The CNN model is trained on a large dataset of annotated face images with and without masks.The face mask detection system works by capturing video frames from a camera feed, detecting faces using Haar cascades or deep learning-based methods, and applying the trained CNN model to classify the presence or absence of masks on each detected face.The system provides real-time feedback, overlaying bounding boxes and labels indicating whether a face is wearing a mask or not.To enhance the system's performance and accuracy, we employ data augmentation techniques during training, which help in handling variations in lighting conditions, poses, and backgrounds.Additionally, we optimize the model for real-time execution, taking advantage of hardware acceleration technologies like GPU processing when available.Through experimental evaluations on various video streams and datasets, our system demonstrates high accuracy and efficiency in real-time face mask detection.It offers a reliable solution for monitoring mask compliance in public spaces, such as airports, hospitals, retail stores, and public transportation, contributing to the prevention of the spread of infectious diseases.