RF Sensing For Smoking Detection At Oil Fields

Hameed, H., Azam, N., Usman, M., Abbas, H. , Imran, M. A. and Abbasi, Q. H. (2022) RF Sensing For Smoking Detection At Oil Fields. In: 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Denver, CO, USA, 10-15 Jul 2022, pp. 944-945. ISBN 9781665496582 (doi: 10.1109/AP-S/USNC-URSI47032.2022.9887288)

Abstract

In this paper, an ultra-wideband (UWB) Radar sensor is used to detect human gestures while smoking or vaping in potentially dangerous areas such as an oil field or a gas station. Existing smoking detection systems are primarily camera-based, which has a number of drawbacks, including poor illumination, training issues with longer video sequence data, and major privacy concerns. The data collected from a UWB Radar is represented in the form of spectrograms. Three classes are considered, namely cigarette, vape and when the subject is not smoking. InceptionV3, VGG19, and VGG16 deep learning algorithms are used to extract spatiotemporal information from the Spectrogram. Finally, by classifying the Spectrograms into the considered gestures, the smoking and/or vaping is accurately identified. The simulation results show that InceptionV3 can achieve a maximum classification accuracy of 90.00%.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Hameed, Mrs Hira and Imran, Professor Muhammad and Usman, Dr Muhammad and Abbas, Dr Hasan and Azam, Ms Naila and Abbasi, Professor Qammer
• Authors: Hameed, H., Azam, N., Usman, M., Abbas, H., Imran, M. A., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• ISSN: 1947-1491
• ISBN: 9781665496582
• Published Online: 21 September 2022
• Copyright Holders: Copyright © 2022 IEEE
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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