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@article{204511,
        author = {Kartikey Sharma and Dr. Deepak Kumar Gupta and Mr. Gaurav Kumar and Mr. Ranjeet Kumar Singh},
        title = {An AI-Enabled Vision System for Mask Compliance Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {3558-3565},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204511},
        abstract = {Automated mask detection is valued in public health, workplace, and controlled environments where manual supervision is neither practical nor cost effective. In this paper, we detail the development of a camera-based monitoring system that utilizes the rapid advancement of cheap and readily available computer processing power and deep neural networks to detect the presence of human faces and masks. Alerts will be sent to the system in the case of mask absence. Recent surveys and papers concerning face mask detection detail the evolution of face mask detection to incorporate rapid, high-deliverable, and real-time computer vision systems. In the proposed design, a practical system architecture is described in the context of real-world, live, camera-based workflow systems. This framework is designed to utilize the real-time monitoring of systems, logging of events, and the issuing of alerts, all while maintaining the use of low-cost monitoring devices. Further, ethical concerns arising from the monitoring of systems, to include privacy and fairness concerns, are briefly addressed. Through the use of confusion matrices, latency charts, and other monitoring tools, we detail the design of a monitoring system from a practical standpoint and urge the need for such systems in healthcare settings and other areas of high traffic, as well.},
        keywords = {Mask compliance monitoring, computer vision, deep learning, YOLO, face-mask detection, edge deployment, surveillance analytics.},
        month = {June},
        }