Mitigating Infectious Disease Transmission with Face Mask Detection Using Machine Learning
Adewale Soetan, Lei Zhang · 2023
Face masks are crucial for controlling the spread of dangerous diseases, as the global epidemic has highlighted. In this study, we describe an innovative method for automatic facemask identification in public areas that combines the activation functions of MobileNet with Scaled Exponential Linear Units (SELUs). Our suggested model is a realistic option for wider adoption because it is small, effective, and capable of achieving real-time performance on edge devices. The MobileNet design, which strikes a compromise between computational complexity and precision, serves as the foundation of our model. By using SELUs, which address the disappearing and exploding gradient problem typical of deep learning models, we marginally enhanced the model’s performance.