Lightweight CNN-Based Deep Neural Networks Application in Safety Measurement

Lua, W. K. H., Yau, P. C.Y. , Seow, C. K. and Dennis, W. (2022) Lightweight CNN-Based Deep Neural Networks Application in Safety Measurement. In: 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), Chengdu, China, 19-21 Aug 2022, ISBN 9781665499163 (doi: 10.1109/PRAI55851.2022.9904161)

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285976.pdf - Accepted Version



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.

Item Type:Conference Proceedings
Additional Information:This research is supported by the Macao Polytechnic University research grant (Project code: RP/FCA-02/2022). The research of the fourth author is also supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT), Korea (No. 2020R1F1A1A01070666).
Glasgow Author(s) Enlighten ID:Yau, Dr Peter C Y and Seow, Dr Chee Kiat
Authors: Lua, W. K. H., Yau, P. C.Y., Seow, C. K., and Dennis, W.
Subjects:T Technology > T Technology (General)
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2022 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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