Real-Time Mask Detection Using Artificial Intelligence for Global Pandemic Protection
Keywords:
ADAM optimization algorithm, Adaptive and efficient convergence, Artificial intelligence & machine learning, Computer vision, Convolutional Neural Networks (CNNs), MobileNetV2, Sensors based systems, World Health Organization (WHO)Abstract
Alterations in global lifestyles have been extensive, with the widespread utilization of face masks emerging as a significant aspect. The challenge lies in identifying individuals not adhering to mask-wearing protocols, a predicament exacerbated by the COVID-19 pandemic's global impact. This innovation holds applicability across diverse settings like educational institutions, healthcare facilities, financial institutions, airports, and more, serving as a digitized surveillance mechanism. This endeavour centres on leveraging image processing and deep learning to discern individuals into two distinct categories: those wearing masks and those without. This method entails the utilization of Python libraries such as OpenCV, TensorFlow, and Keras. Particularly, Convolutional Neural Networks, a subset of Deep Neural Networks, play a pivotal role in training the models essential for the project's success.
The technology enables efficient remote monitoring, allowing personnel to oversee compliance and dispense instructions from a distant location. This advancement not only streamlines the monitoring process but also enhances its efficacy, illustrating the dynamic synergy between technological innovation and public health management.
