AI-Powered Face Mask Detection Utilizing MobileNetV2 for Health Monitoring

Abstract

The COVID-19 pandemic has highlighted the critical need for face masks to prevent virus transmission. Ensuring consistent mask usage in crowded public spaces remains a challenge, especially with manual monitoring methods that are inefficient and prone to error. To address this, this research introduces a real-time face mask detection system leveraging MobileNet-V2, a lightweight and efficient deep learning model known for its high performance in image classification tasks. The system utilizes a dataset from Kaggle comprising 11,792 images, divided into training (10,000), validation (800), and testing (992) sets. MobileNet-V2 was fine-tuned for this task, using its inverted residual layers to extract features and enhance performance effectively. Data augmentation techniques were applied to improve the model’s ability to generalize across diverse scenarios. The MobileNet-V2 model achieved an impressive 98.69% accuracy on the testing dataset, demonstrating exceptional reliability in identifying individuals wearing masks versus those without. Standard evaluation metrics, including precision, recall, and a confusion matrix, confirmed its robustness. This system’s ability to operate in real-time makes it ideal for public health surveillance in environments such as airports, shopping malls, and public transport. The proposed face mask detection system is both accurate and scalable, offering an efficient solution for enforcing mask-wearing protocols in public spaces. The system’s integration of advanced deep learning techniques ensures its reliability in real-time monitoring, contributing to better public health management. Future work will focus on further optimizing the model and expanding its application to other health-related monitoring tasks, enhancing its value for public health surveillance.