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CNN-Based Smoker Classification and Detection in Smart City Application
To better regulate smoking in no-smoking areas, we present a novel AI-based surveillance system for smart cities. In this paper, we intend to solve the issue of no-smoking area surveillance by introducing a framework for an AI-based smoker detection system for no-smoking areas in a smart city. Moreo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8839928/ https://www.ncbi.nlm.nih.gov/pubmed/35161637 http://dx.doi.org/10.3390/s22030892 |
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author | Khan, Ali Khan, Somaiya Hassan, Bilal Zheng, Zhonglong |
author_facet | Khan, Ali Khan, Somaiya Hassan, Bilal Zheng, Zhonglong |
author_sort | Khan, Ali |
collection | PubMed |
description | To better regulate smoking in no-smoking areas, we present a novel AI-based surveillance system for smart cities. In this paper, we intend to solve the issue of no-smoking area surveillance by introducing a framework for an AI-based smoker detection system for no-smoking areas in a smart city. Moreover, this research will provide a dataset for smoker detection problems in indoor and outdoor environments to help future research on this AI-based smoker detection system. The newly curated smoker detection image dataset consists of two classes, Smoking and NotSmoking. Further, to classify the Smoking and NotSmoking images, we have proposed a transfer learning-based solution using the pre-trained InceptionResNetV2 model. The performance of the proposed approach for predicting smokers and not-smokers was evaluated and compared with other CNN methods on different performance metrics. The proposed approach achieved an accuracy of 96.87% with 97.32% precision and 96.46% recall in predicting the Smoking and NotSmoking images on a challenging and diverse newly-created dataset. Although, we trained the proposed method on the image dataset, we believe the performance of the system will not be affected in real-time. |
format | Online Article Text |
id | pubmed-8839928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88399282022-02-13 CNN-Based Smoker Classification and Detection in Smart City Application Khan, Ali Khan, Somaiya Hassan, Bilal Zheng, Zhonglong Sensors (Basel) Article To better regulate smoking in no-smoking areas, we present a novel AI-based surveillance system for smart cities. In this paper, we intend to solve the issue of no-smoking area surveillance by introducing a framework for an AI-based smoker detection system for no-smoking areas in a smart city. Moreover, this research will provide a dataset for smoker detection problems in indoor and outdoor environments to help future research on this AI-based smoker detection system. The newly curated smoker detection image dataset consists of two classes, Smoking and NotSmoking. Further, to classify the Smoking and NotSmoking images, we have proposed a transfer learning-based solution using the pre-trained InceptionResNetV2 model. The performance of the proposed approach for predicting smokers and not-smokers was evaluated and compared with other CNN methods on different performance metrics. The proposed approach achieved an accuracy of 96.87% with 97.32% precision and 96.46% recall in predicting the Smoking and NotSmoking images on a challenging and diverse newly-created dataset. Although, we trained the proposed method on the image dataset, we believe the performance of the system will not be affected in real-time. MDPI 2022-01-24 /pmc/articles/PMC8839928/ /pubmed/35161637 http://dx.doi.org/10.3390/s22030892 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Khan, Ali Khan, Somaiya Hassan, Bilal Zheng, Zhonglong CNN-Based Smoker Classification and Detection in Smart City Application |
title | CNN-Based Smoker Classification and Detection in Smart City Application |
title_full | CNN-Based Smoker Classification and Detection in Smart City Application |
title_fullStr | CNN-Based Smoker Classification and Detection in Smart City Application |
title_full_unstemmed | CNN-Based Smoker Classification and Detection in Smart City Application |
title_short | CNN-Based Smoker Classification and Detection in Smart City Application |
title_sort | cnn-based smoker classification and detection in smart city application |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8839928/ https://www.ncbi.nlm.nih.gov/pubmed/35161637 http://dx.doi.org/10.3390/s22030892 |
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