Mask Detection

Mask detection research focuses on automatically identifying the presence, type, and proper wearing of face masks using computer vision techniques, primarily to aid in public health monitoring and security applications. Current research emphasizes the development and optimization of deep learning models, particularly Convolutional Neural Networks (CNNs) like YOLO and SSD, and Vision Transformers (ViTs), often incorporating transfer learning and data augmentation strategies to improve accuracy and speed, even on resource-constrained devices. These advancements are crucial for real-time applications such as crowd monitoring and access control, improving the efficiency and effectiveness of public health measures and security systems. Furthermore, research extends to addressing challenges like 3D mask detection and robust performance in low-resolution or thermal imagery.