The enforcement of protective measures, such as wearing face masks, has become essential in mitigating the spread of airborne diseases. Traditional methods of monitoring compliance can be labor-intensive and inefficient, leading to the need for automated solutions. Artificial intelligence (AI) and machine learning (ML) have emerged as effective tools for real-time face mask detection, improving efficiency and accuracy in public safety enforcement. This study presents the development and deployment of an intelligent system that utilizes deep learning techniques for mask detection in various environmental conditions. The proposed model is trained on diverse datasets, ensuring robustness against variations in lighting, occlusion, and mask types. By integrating convolutional neural networks (CNNs) and computer vision, the system accurately classifies individuals as masked or unmasked in real-time video streams. The research discusses model architecture, data pre-processing, and implementation strategies while addressing key challenges such as false detections and performance optimization. The findings demonstrate the potential of AI-driven surveillance systems in promoting adherence to health regulations, reducing manual monitoring efforts, and enhancing public safety. Future advancements may focus on improving accuracy, optimizing computational efficiency, and integrating additional features such as thermal screening and voice alerts for broader applications.
Deep Learning, AI-Based Surveillance, Face Mask Compliance, Computer Vision, Public Health Monitoring, Real-Time Detection
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