Smart City Gnosys
Smart city article details
| Title | Automatic Face Mask Detection System In Public Transportation In Smart Cities Using Iot And Deep Learning |
|---|---|
| ID_Doc | 11318 |
| Authors | Kumar T.A.; Rajmohan R.; Pavithra M.; Ajagbe S.A.; Hodhod R.; Gaber T. |
| Year | 2022 |
| Published | Electronics (Switzerland), 11, 6 |
| DOI | http://dx.doi.org/10.3390/electronics11060904 |
| Abstract | The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| Author Keywords | Artificial intelligence; COVID-19; Deep learning; Face mask system; Smart bus |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
25979  | 0.919 | Himeur Y.; Al-Maadeed S.; Varlamis I.; Al-Maadeed N.; Abualsaud K.; Mohamed A. | Face Mask Detection In Smart Cities Using Deep And Transfer Learning: Lessons Learned From The Covid-19 Pandemic | Systems, 11, 2 (2023) |
| 11319 | 0.908 | Alsalamah M.S.I. | Automatic Face Mask Identification In Saudi Smart Cities: Using Technology To Prevent The Spread Of Covid-19 | Information Sciences Letters, 12, 6 (2023) |
| 1756 | 0.907 | Rautaray P.K.; Pattanayak B.K.; Mohanty M.N.; Mohanty B. | A Framework For Detection Of Face Mask Using Deep Learning Approach With Internet Of Things (Iot) | Journal of Advanced Research in Applied Sciences and Engineering Technology, 47, 2 (2025) |
| 30516 | 0.894 | Draughon G.T.S.; Sun P.; Lynch J.P. | Implementation Of A Computer Vision Framework For Tracking And Visualizing Face Mask Usage In Urban Environments | 2020 IEEE International Smart Cities Conference, ISC2 2020 (2020) |
| 7694 | 0.889 | Rahman M.M.; Manik M.M.H.; Islam M.M.; Mahmud S.; Kim J.-H. | An Automated System To Limit Covid-19 Using Facial Mask Detection In Smart City Network | IEMTRONICS 2020 - International IOT, Electronics and Mechatronics Conference, Proceedings (2020) |
| 19652 | 0.887 | Herath H.M.K.K.M.B.; Karunasena G.M.K.B.; Herath H.M.W.T. | Development Of An Iot Based Systems To Mitigate The Impact Of Covid-19 Pandemic In Smart Cities | Studies in Computational Intelligence, 971 (2021) |
| 57860 | 0.876 | Alexiou M.; Ktistakis I.P.; Goodman G. | Towards A Masked Face Recognition Algorithm: A Novel Rule Based Hybrid Algorithm | 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2021 (2021) |
| 36445 | 0.863 | Bhale Y.; Agrawal N.; Kelwa S. | Mask Detection Using Computer Vision | Proceedings of the 2021 10th International Conference on System Modeling and Advancement in Research Trends, SMART 2021 (2021) |
| 19193 | 0.863 | Daya Sagar K.V.; Kamesh D.B.K.; Srinivasa Rao T.; Krishna C.V.M. | Detecting Fake Faces In Smart Cities Security Surveillance Using Image Recognition And Convolutional Neural Networks | ECS Transactions, 107, 1 (2022) |
| 61624 | 0.859 | Issaouı I.; Selmi A. | Weighted Soft Discernibility Matrix With Deep Learning Assisted Face Mask Detection For Smart City Environment | International Journal of Neutrosophic Science, 25, 1 (2025) |