@inproceedings{c1de01836ca74ab3973c1f48d9d16032,
title = "Lightweight CNN-Based Deep Neural Networks Application in Safety Measurement",
abstract = "Inspired by the face covering period in the past two years, COVID-19 pandemic has resulted in the mandate of public safety measures such as face mask-wearing in many countries. This paper provides a preliminary feasibility planning on how Artificial Intelligence (AI), Computer Vision (CV) and the Internet of Things (IoT) can work together to implement a face-mask detection system as a public health safety solution. This paper reviews how edge computing can overcome traditional cloud computing issues. This work also examines the current state of computer vision, convolutional neural networks and their potential application in the health and safety domain. This writing serves as an interim report on how the lightweight CNNs and single-shot detectors such as YOLOv5 variants with SSD to train and deploy an object detection system.",
keywords = "AI, CNNs, COVID-19, IoT, artificial intelligence, computer vision, deep learning, edge computing, health, internet-of-things, mask, neural network, safety",
author = "{Kai Heng Lua}, Wilbur and {Chunyu Yau}, Peter and {Kiat Seow}, Chee and Dennis Wong",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022 ; Conference date: 19-08-2022 Through 21-08-2022",
year = "2022",
doi = "10.1109/PRAI55851.2022.9904161",
language = "English",
series = "2022 5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "455--459",
booktitle = "2022 5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022",
address = "United States",
}
