Optimized Edge-Deployable Computer Vision for Real‑Time Face Mask and Social Distancing Compliance Detection in Diverse Pandemic Environments

Sivakumar Ponnusamy, Prasanna Kumar Yekula, G. Visalaxi, K. Kokulavani, Lokasani Bhanuprakash, Murali P.

2025

Abstract

This study presents an optimized, edge‑deployable computer vision framework for real‑time detection of face masks and social distancing violations in diverse pandemic environments. By integrating a lightweight multi‑task neural network with dynamic perspective correction and quantization‑aware training, the proposed system achieves high accuracy and low latency on resource‑constrained hardware. Advanced data augmentation including varied lighting, occlusion, and angle simulations enhances robustness against real‑world conditions, while an efficient single‑shot detector reduces computational overhead. Extensive evaluation on multiple public datasets and live demonstrations on embedded devices demonstrate consistent mask classification accuracy above 98 % and social distance estimation errors below 5 cm, all at over 25 FPS. The unified architecture simplifies deployment and maintenance, addressing common challenges such as small‑face detection, varied mask styles, and perspective distortion. This approach enables scalable, cost‑effective monitoring solutions for public spaces, healthcare facilities, and transportation hubs without reliance on cloud infrastructure, preserving privacy and ensuring rapid response to safety violations.

Paper Citation

in Harvard Style

Ponnusamy S., Yekula P., Visalaxi G., Kokulavani K., Bhanuprakash L. and P. M. (2025). Optimized Edge-Deployable Computer Vision for Real‑Time Face Mask and Social Distancing Compliance Detection in Diverse Pandemic Environments. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 578-583. DOI: 10.5220/0013869500004919

in Bibtex Style

@conference{icrdicct`2525,
author={Sivakumar Ponnusamy and Prasanna Kumar Yekula and G. Visalaxi and K. Kokulavani and Lokasani Bhanuprakash and Murali P.},
title={Optimized Edge-Deployable Computer Vision for Real‑Time Face Mask and Social Distancing Compliance Detection in Diverse Pandemic Environments},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={578-583},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013869500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Optimized Edge-Deployable Computer Vision for Real‑Time Face Mask and Social Distancing Compliance Detection in Diverse Pandemic Environments
SN - 978-989-758-777-1
AU - Ponnusamy S.
AU - Yekula P.
AU - Visalaxi G.
AU - Kokulavani K.
AU - Bhanuprakash L.
AU - P. M.
PY - 2025
SP - 578
EP - 583
DO - 10.5220/0013869500004919
PB - SciTePress