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ABSTRACT
This paper introduces a convolutional network to address the significant lifestyle shifts induced by global changes, notably the ubiquitous adoption of masks due to the COVID-19 pandemic. The widespread use of masks has become integral to safeguarding individuals worldwide. However, detecting non-compliance with mask-wearing protocols poses a formidable challenge amid the pandemic’s outbreak.
This project aims to tackle this challenge by leveraging advanced technology applicable across diverse environments such as schools, hospitals, banks, airports, and other surveillance-dependent areas. The primary objective is the detection and classification of individuals into two distinct categories: those wearing masks and those without masks. This segregation is achieved through image processing techniques and the sophisticated capabilities of deep learning, specifically employing convolutional neural networks (CNN).
The CNN model plays a pivotal role in this framework, efficiently reducing data weight during processing. This technology not only ensures effective identification but also streamlines the computational load, making it feasible for real-time application in live video streams.
The proposed system’s potential extends beyond routine surveillance scenarios. It offers the capability to remotely detect whether an individual, situated in a remote area, adheres to mask-wearing mandates. By accessing live video streams, this technology enables instant determination of mask compliance without requiring physical presence or human intervention.
Ultimately, this innovative fusion of image processing and CNN-based deep learning signifies a paradigm shift in surveillance and compliance monitoring.
