Real-time face mask detection in mass gatherings to reduce Covid-19 spread

• Autorzy: Soner Swapnil , Litoriya Ratnesh , Khatri Ravi , Hussain Ali Asgar , Pagrey Shreyas , Kushwaha Sunil Kumar

Identyfikatory

DOI

10.14313/JAMRIS/1‐2023/7

• Języki publikacji: EN

Abstrakty

EN

The Covid 19 (coronavirus) pandemic has become one of the most lethal health crises worldwide. This virus gets transmitted from a person by respiratory droplets when they sneeze or when they speak. According to leading and well‐known scientists, wearing face masks and maintain‐ ing six feet of social distance are the most substantial protections to limit the virus’s spread. In the proposed model we have used the Convolutional Neural Network (CNN) algorithm of Deep Learning (DL) to ensure efficient real‐time mask detection. We have divided the system into two parts—1. Train Face Mask Detector 2. Apply Face Mask Detector—for better understanding. This is a real‐ time application that is used to discover or detect the person who is wearing a mask at the proper position or not, with the help of camera detection. The system has achieved an accuracy of 99% after being trained with the dataset, which contains around 1376 images of width and height 224×224 and also gives the alarm beep message after the detection of no mask or improper mask usage in a public place.

Słowa kluczowe

EN

Covid; machine learning; face mask detection; Deep Learning

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2023
• Tom: Vol. 17, No. 1
• Strony: 51--58
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Soner Swapnil

• – Jaypee University of Engineering & Technology, Raghogarh, Guna (M.P.), India

autor

Litoriya Ratnesh

• Medi-Caps University, Indore, India

autor

Khatri Ravi

• I Nurture Education Solutions Pvt. Ltd. India

autor

Hussain Ali Asgar

• Medi-Caps University, Indore, India

autor

Pagrey Shreyas

• Chameli Devi Group of Institutions, Indore, India

autor

Kushwaha Sunil Kumar

• Medi-Caps University, Indore, India

Bibliografia

• [1] Pranad Munjal, Vikas Rattan, Rajat Dua, and Varun Malik. “Real‐Time Face Mask Detection using Deep Learning” Journal of Technology Management for Growing Economies, vol. 12, no. 1 (2021), pp. 25–31. DOI: 10.15415/jtmge. 2021.121003.
• [2] Mohammad Marufur Rahman, Saifuddin Mahmud, Md. Motaleb Hossen Manik, Jong‐Hoon Kim, and Md. Milon Islam. “An Automated System to Limit COVID‐19 Using Facial Mask Detection in Smart City Network” Published in: 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). DOI: 10.1109/IEMTRON‐ICS51293.2020.9216386.
• [3] A. Nieto‐Rodríguez, M. Mucientes, and V. M. Brea. “System for Medical Mask Detection in the Operating Room Through Facial Attributes”. In Pattern Recognition and Image Analysis, Roberto Paredes, Jaime S. Cardoso, and Xosé M. Pardo (Eds.) 2015, Springer International Publishing, Cham, 138–145.
• [4] A. Das, M. Ansari, and R. Basak. “Covid‐19 Face Mask Detection Using TensorFlow, Keras and OpenCV.” 2020 IEEE 17th India Council International Conference (INDICON), New Delhi, India. 2020. DOI: 10.1109/INDICON49873.2020.934 2585.
• [5] S. V. Militante and N. V. Dionisio. “Real‐Time Face Mask Recognition with Alarm System using Deep Learning”. 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, Malaysia. 2020. DOI: 10.1109/ICS‐GRC49013.2020.9232610.
• [6] F. M. Javed Mehedi Shamrat, Sovon Chakraborty, Md. Masum Billah, Md. Al Jubair, Md Saidul Islam, and Rumesh Ranjan, “Face Mask Detection using Convolutional Neural Network (CNN) to reduce the spread of Covid‐19”. 5th International Conference on Trends in Electronics and Informatics (ICOEI 2021) Tirunelveli, India, 3‐5, June 2021. DOI: 10.1109/ICOEI51242.2021.9452836.
• [7] Wadii Boulila, Adel Ammar, Bilel Benjdira, and Anis Koubaa. “Securing the Classification of COVID‐19 in Chest X‐ray Images: A Privacy‐Preserving Deep Learning Approach.” Image and Video Processing (eess.IV). DOI: 10.48550/arXiv.2203.07728.
• [8] M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa. “A Hybrid Deep Transfer Learning Model with Machine Learning Methods for Face Mask Detection in the Era of the COVID‐19 Pandemic. Measurement, 167, 2021. 108288. DOI: 10.1016/j.
• [9] S. Soner, R. Litoriya, and P. Pandey. “Exploring Blockchain and Smart Contract Technology for Reliable and Secure Land Registration and Record Management.” Wireless Pers Commun 121, 2495–2509. 2021. DOI: 10.1007/s11277‐021‐08833‐1.
• [10] P. Pandiyan. “Social Distance Monitoring and Face Mask Detection Using Deep Neural Network.” (2020, December 17). Retrieved from: https://www.researchgate.net/publication/347439579_Social_Distance_Monitoring_and_Face_Mask_Detection_Using_Deep_Neural_Network, DOI: 10.15415/jtmge.2021.121003.
• [11] S. Soner, R. Litoriya, and P. Pandey. “Making Toll Charges Collection Efficient and Trustless: A Blockchain‐Based Approach” 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, DOI: 10.1109/ICAC3N53548. 2021.9725447.
• [12] H. Vagrecha, A. Tuteja, A. S. Mandloi, A. Dube, and S. Soner. “Coders Hub ML Edu. Platform Systems (May 24, 2021).” Proceedings of the International Conference on Smart Data Intelligence (ICSMDI 2021). Available at SSRN: https://ssrn.com/abstract=3851962 or DOI: 10.2139/ssrn.3851962.
• [13] X. Wang, X. Le, and Q. Lu. “Analysis of China’s Smart City Upgrade and Smart Logistics Development under the COVID‐19 Epidemic,” J. Phys. Conf. Ser., vol. 1570, p. 012066, 2020. DOI: 10.3390/s21113838.
• [14] J. Won Sonn, and J. K. Lee. “The smart city as time‐space cartographer in COVID‐19 control: the South Korean strategy and democratic control of surveillance technology,” Eurasian Geogr. Econ., pp. 1‐11, May. 2020. DOI: 10.1016/j.scitotenv.2020.142391.
• [15] P. Ghosh, F. M. Javed Mehedi Shamrat, S. Shultana, S. Afrin, A. A. Anjum, and A. A. Khan. “Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm,” 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI‐NLP), Bangkok, Thailand, 2020, pp. 1‐6. DOI: 10.1109/iSAI‐NLP51646.2020.9376787.
• [16] Preeti Nagrath, Rachna Jain, Agam Madan, Rohan Arora, Piyush Kataria, and Jude Hemanth. 2021. “SSDM V2: A real time DNN‐based face mask detection system using single shot multibox detector and MobileNetV2.” Sustainable Cities and Society 66 (2021), 102692. DOI: 10.1016/j.scs.2020.102692.
• [17] Institute for Health Metrics and Evaluation.2021. COVID‐19 Projections. Retrieved April 27, 2021. page 1665. DOI: 10.1016/S0140‐6736 (21)02143‐7.
• [18] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. “You Only Look Once: Unified, Real‐Time Object Detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).”2016. 779–788. DOI: 10.1109/CVPR.2016.91.
• [19] M. V. Manorj Kumar et al. “Detection of Covid‐19 Using Deep Learning Technique and Cost Effectiveness Evaluation: A Survey”. Front. Artif. Intell., 27 May 2022 Sec. Medicine and Public Health. DOI: 10.3389/frai.2022.912022.
• [20] Zaid Abdi Alkareem Alyasseri et al. “Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches” Expert Syst. 2022 Mar; 39(3): e12759. PMCID: PMC8420483 PMID: 34511689. DOI: 10.1111/exsy.12759.
• [21] S. V. Kogilavani et al. “COVID‐19 Detection Based on Lung CT Scan Using Deep Learning Techniques.” Research Article | Open Access Volume 2022 | Article ID 7672196 | DOI: 10.1155/2022/7672196.
• [22] Mohamad Alkhodari, and Ahsan, H. Khandoker. “Detection of COVID‐19 in smartphone‐based breathing recordings: A pre‐screening deep learning tool.” Published: January 13, 2022. DOI: 10.1371/journal.pone.0262448.
• [23] Muhammad Imad et al. “IoT Based Machine Learning and Deep Learning Platform for COVID‐19 Prevention and Control: A Systematic Review” January 2022. In book: AI and IoT for Sustainable Development in Emerging Countries (pp. 523‐536). DOI: 10.1007/978‐3‐030‐906 18‐4_26.
• [24] https://www.researchgate.net/publication/344725412_Covid19_Face_Mask_Detection_Using_TensorFlow_Keras_and_OpenCV#pf2.
• [25] S. Soner, J. Kukade, “Autonomous Anomaly Detection System for Crime Monitoring and Alert Generation”, Journal of Automation, Mobile Robotics and Intelligent Systems 16(1), 62‐71, DOI: 10.14313/JAMRIS/1‐2022/7.
• [26] S. Soner, R. Litoriya, P. Pandey, “Making Toll Charges Collection Efficient and Trustless: A Blockchain‐Based Approach” 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), DOI: 10.1109/ICAC3N56670. 2022. 16‐17 Dec. 2022.
• [27] S. Soner, A. Jain, A. Tripathi, R. Litoriya. “A novel approach to calculate the severity and priority of bugs in software projects”, 2010 2nd International conference on education technology and computer, Volume 2, Pages V2‐50–V2‐54 Publisher IEEE, DOI: 10.1109/ICETC.2010.5529438.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).