The week's pick
Reseach Article
The Right Face Mask Detection System using Deep Learning Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 51 |
| Year of Publication: 2022 |
| Authors: Bagus Hendra Saputra, Wahyu Kusuma Raharja |
10.5120/ijca2022921922
|
Bagus Hendra Saputra, Wahyu Kusuma Raharja . The Right Face Mask Detection System using Deep Learning Methods. International Journal of Computer Applications. 183, 51 ( Feb 2022), 20-26. DOI=10.5120/ijca2022921922
Abstract
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by acute respiratory syndrome (SARS-CoV-2). As of June 23, 2021, as a result of the virus infected patients reached 178,837,204 and 3,880,450 died. Therefore, WHO (World Health Organization) imposes health protocols that must be implemented by all individuals throughout the country, based on that the government of the Republic of Indonesia through the Decree of the Minister of Health of the Republic of Indonesia Number HK.01.07 / MENKES / 382/2020 concerning health protocols for people in places and public facilities in order to prevent and control corona virus disease 2019 (COVID-19) which is mandatory to wear a mask when out of the house, Maintain a strict social distance in public places of at least 1 meter and often wash hands or sanitize hands with disinfectant. In this research, the researchers create a system to conduct supervision on the community to be able to comply with health protocols, especially the use of appropriate masks. This system is called the right Face Mask Detection System using deep learning methods with MobileNetV2 module and Tensorflow framework. Based on 90 tests conducted obtained the results of 7 invalid tests, so that the accuracy of the system that has been built has an average of 86.95%.
References
- S. Chavez, B. Long, A. Koyfman, and S. Y. Liang, “Coronavirus Disease (COVID-19): A primer for emergency physicians,” Am. J. Emerg. Med., vol. 44, pp. 220–229, Jun. 2021, doi: 10.1016/j.ajem.2020.03.036.
- “WHO Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data.” https://covid19.who.int/ (accessed Jun. 24, 2021).
- S. W. Sim, K. S. P. Moey, and N. C. Tan, “The use of facemasks to prevent respiratory infection: A literature review in the context of the Health Belief Model,” Singapore Med. J., vol. 55, no. 3, pp. 160– 167, 2014, doi: 10.11622/smedj.2014037.
- B. J. Cowling et al., “Facemasks and hand hygiene to prevent influenza transmission in households: A cluster randomized trial,” Annals of Internal Medicine, Oct. 06, 2009. https://www.acpjournals.org/doi/pdf/10.7326/0003- 4819-151-7-200910060-00142 (accessed Jun. 24, 2021).
- S. M. Tracht, S. Y. Del Valle, and J. M. Hyman, “Mathematical modeling of the effectiveness of facemasks in reducing the spread of novel influenza a (H1N1),” PLoS One, vol. 5, no. 2, p. 9018, Feb. 2010, doi: 10.1371/journal.pone.0009018.
- T. Jefferson et al., “Physical interventions to interrupt or reduce the spread of respiratory viruses,” Cochrane Database of Systematic Reviews, Nov. 20, 2020. https://www.cochranelibrary.com/cdsr/doi/10.1002/1 4651858.CD006207.pub5/full (accessed Jun. 24, 2021).
- “Cara Memakai Masker yang Benar,” Feb. 04, 2020. https://promkes.kemkes.go.id/cara-memakai-masker- yang-benar (accessed Oct. 09, 2021).
- D. Ullala Mata, “FACE MASK DETECTION USING CONVOLUTIONAL NEURAL NETWORK,” J. Nat. Remedies, vol. 21, no. 12, pp. 14–19, Apr. 2021, Accessed: Jun. 29, 2021. [Online]. Available: http://www.jnronline.com/ojs/index.php/about/article/view/784.
- S. Yadav, “Deep Learning based Safe Social Distancing and Face Mask Detection in Public Areas for COVID-19 Safety Guidelines Adherence,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 8, no. 7, pp. 1368– 1375, Jul. 2020, doi: 10.22214/ijraset.2020.30560.
- S. K. Addagarla, G. Kalyan Chakravarthi, and P. Anitha, “Real time multi-scale facial mask detection and classification using deep transfer learning techniques,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 4402–4408, 2020, doi: 10.30534/ijatcse/2020/33942020.
- O. Gurav, “Face Mask Detection Dataset | Kaggle.” https://www.kaggle.com/omkargurav/face-mask- dataset (accessed Jul. 12, 2021).
- “GitHub - X-zhangyang/Real-World-Masked-Face- Dataset: Real-World Masked Face Dataset，罩人脸数据集.” https://github.com/X- zhangyang/Real-World-Masked-Face-Dataset (accessed Jul. 12, 2021).
- E. Yuliani, A. N. Aini, and C. U. Khasanah, “Perbandingan Jumlah Epoch Dan Steps Per Epoch Pada Convolutional Neural Network Untuk Meningkatkan Akurasi Dalam Klasifikasi Gambar,” J. Inf. J. Penelit. dan Pengabdi. Masy., vol. 5, no. 3, pp. 23–27, Jan. 2019, doi: 10.46808/INFORMA.V5I3.140.
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