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CNN-Based Smoker Classification and Detection in Smart City Application
Content Provider | MDPI |
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Author | Khan, Ali Khan, Somaiya Hassan, Bilal Zheng, Zhonglong |
Copyright Year | 2022 |
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. |
Starting Page | 892 |
e-ISSN | 14248220 |
DOI | 10.3390/s22030892 |
Journal | Sensors |
Issue Number | 3 |
Volume Number | 22 |
Language | English |
Publisher | MDPI |
Publisher Date | 2022-01-24 |
Access Restriction | Open |
Subject Keyword | Sensors Industrial Engineering Information and Library Science Ai-based Surveillance Smoker Detection Dataset Smoker Classification Transfer Learning |
Content Type | Text |
Resource Type | Article |