Enhancing the Public Health and Automated Mask Detection

Abstract

Abstract: Response to the global pandemic, in this project have developed an innovative mask recognition system using deep learning and computer vision, based on the MobileNetV2 convolutional neural network architecture with a custom classification head. This system effectively distinguishes individuals wearing masks from those without by employing careful pre-processing, data augmentation, and model training. Through meticulous parameter tuning, achieved impressive performance metrics: a training accuracy of 98%, validation accuracy of 97%, and a balanced F1-score of 96%. This well-rounded model strikes a balance between precision and recall, minimizing false positives and false negatives. It's a significant contribution to public health and safety, poised to enhance mask compliance and collective well-being in our changing world.