Application of Object Mask Detection Using the Convolution Neural Network (CNN)
Abstract
The spread of Coronavirus Disease (Covid-19) is still a serious problem that we are currently facing. Spread occurred very quickly through the face-to-face interaction process. The face-to-face interaction process that occurs both in public spaces and in closed spaces has a great risk of transmitting the Covid-19 virus. One of the efforts to deal with the spread of the Covid-19 virus is to increase the use of masks in both public and closed spaces. On the basis of this, this study aims to develop an object detection process in image processing techniques. Object detection development using the convolution neural network (CNN) method to provide optimal output. CNN can process the input image, which is converted into a pixel matrix and then sent to the convolution layer. The research data set consists of 2000 images of masks and not masks. The images were obtained from open sources, github.com and kaggle.com. The results of the study present a system capable of detecting masks in real time. CNN provides very good performance with an accuracy rate of 99.05%. With these results, the contribution of this research can be used in the monitoring of public services for the community to increase the use of masks.
Downloads
References
S. Arita, J. Grant, S. Sydow, and J. Beckman, “Has global agricultural trade been resilient under coronavirus (COVID-19)? Findings from an econom[1] F. D. A. Pinasti, “Analisis dampak pandemi corona virus terhadap tingkat kesadaran masyarakat dalam penerapan protokol kesehatan,” Wellness Heal. Mag., vol. 2, no. 2, pp. 237–249, 2020.
Z. Zahrotunnimah, “Langkah taktis pemerintah daerah dalam pencegahan penyebaran virus Corona Covid-19 di Indonesia,” 2020.
C. Zheng, W. Shao, X. Chen, B. Zhang, G. Wang, and W. Zhang, “Real-world effectiveness of COVID-19 vaccines: a literature review and meta-analysis,” Int. J. Infect. Dis., vol. 114, pp. 252–260, 2022.
D. Irawan, N. Triana, L. Suwarni, and S. Selviana, “Edukasi protokol kesehatan dan strategi pemasaran online melalui program kemitraan masyarakat di era pandemi COVID-19,” JMM (Jurnal Masy. Mandiri), vol. 4, no. 4, pp. 655–662, 2020.
O. J. Watson, G. Barnsley, J. Toor, A. B. Hogan, P. Winskill, and A. C. Ghani, “Global impact of the first year of COVID-19 vaccination: a mathematical modelling study,” Lancet Infect. Dis., vol. 22, no. 9, pp. 1293–1302, 2022.
T. Meihartati, “Pentingnya Protokol Kesehatan Keluar Masuk Rumah Saat Pandemi Covid-19 Dilingkungan Masyarakat Rt 30 Kelurahan Air Hitam, Samarinda, Kalimantan Timur,” Abdimas Med., vol. 1, no. 2, 2020.
R. C. Woodruff et al., “Risk factors for severe COVID-19 in children,” Pediatrics, vol. 149, no. 1, p. e2021053418, 2022.
G. A. Anarki, K. Auliasari, and M. Orisa, “Penerapan Metode Haar Cascade Pada Aplikasi Deteksi Masker,” JATI (Jurnal Mhs. Tek. Inform., vol. 5, no. 1, pp. 179–186, 2021.
J. Nie, L. Kang, Y. Pian, and J. Hu, “The need for more robust research on the effectiveness of masks in preventing COVID-19 transmission,” Future Virol., vol. 17, no. 7, pp. 491–494, 2022.
X. Ma, X.-F. Luo, L. Li, Y. Li, and G.-Q. Sun, “The influence of mask use on the spread of COVID-19 during pandemic in New York City,” Results Phys., vol. 34, p. 105224, 2022.
M. A. S. Ai et al., “Real-time facemask detection for preventing COVID-19 spread using transfer learning based deep neural network,” Electronics, vol. 11, no. 14, p. 2250, 2022.
M. Fakih, S. Oktaviana, E. Nurlaili, D. Febrianto, and N. Nargis, “The Implementation Of Health Protocols On E-Court Systems During The Covid-19 Pandemic Era (In Bandar Lampung City),” Cepalo, vol. 6, no. 1, pp. 23–36, 2022.
Z. Levine and D. J. D. Earn, “Face masking and COVID-19: potential effects of variolation on transmission dynamics,” J. R. Soc. Interface, vol. 19, no. 190, p. 20210781, 2022.
T. A. Kumar, R. Rajmohan, M. Pavithra, S. A. Ajagbe, R. Hodhod, and T. Gaber, “Automatic face mask detection system in public transportation in smart cities using IoT and deep learning,” Electronics, vol. 11, no. 6, p. 904, 2022.
X. Du‐Harpur, F. M. Watt, N. M. Luscombe, and M. D. Lynch, “What is AI? Applications of artificial intelligence to dermatology,” Br. J. Dermatol., vol. 183, no. 3, pp. 423–430, 2020.
D. Nahavandi, R. Alizadehsani, A. Khosravi, and U. R. Acharya, “Application of artificial intelligence in wearable devices: Opportunities and challenges,” Comput. Methods Programs Biomed., vol. 213, p. 106541, 2022.
A. Alam et al., “Object Detection Learning for Intelligent Self Automated Vehicles.,” Intell. Autom. Soft Comput., vol. 34, no. 2, 2022.
S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. Asghar, and B. Lee, “A survey of modern deep learning based object detection models,” Digit. Signal Process., p. 103514, 2022.
Y. Chen, Y. Li, X. Zhang, J. Sun, and J. Jia, “Focal sparse convolutional networks for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5428–5437.
C. Liu, S. ME Sepasgozar, S. Shirowzhan, and G. Mohammadi, “Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms,” Constr. Innov., vol. 22, no. 1, pp. 141–159, 2022.
P. Wu, A. Liu, J. Fu, X. Ye, and Y. Zhao, “Autonomous surface crack identification of concrete structures based on an improved one-stage object detection algorithm,” Eng. Struct., vol. 272, p. 114962, 2022.
R. Karthika and L. Parameswaran, “A novel convolutional neural network based architecture for object detection and recognition with an application to traffic sign recognition from road scenes,” Pattern Recognit. Image Anal., vol. 32, no. 2, pp. 351–362, 2022.
P. Mittal, R. Singh, and A. Sharma, “Deep learning-based object detection in low-altitude UAV datasets: A survey,” Image Vis. Comput., vol. 104, p. 104046, 2020.
Z.-Q. Zhao, P. Zheng, S. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE Trans. neural networks Learn. Syst., vol. 30, no. 11, pp. 3212–3232, 2019.
T. Sharma, B. Debaque, N. Duclos, A. Chehri, B. Kinder, and P. Fortier, “Deep learning-based object detection and scene perception under bad weather conditions,” Electronics, vol. 11, no. 4, p. 563, 2022.
N. Zeng, P. Wu, Z. Wang, H. Li, W. Liu, and X. Liu, “A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection,” IEEE Trans. Instrum. Meas., vol. 71, pp. 1–14, 2022.
Y. Gong, J. Luo, H. Shao, and Z. Li, “A transfer learning object detection model for defects detection in X-ray images of spacecraft composite structures,” Compos. Struct., vol. 284, p. 115136, 2022.
S. Rani, D. Ghai, and S. Kumar, “Object detection and recognition using contour based edge detection and fast R-CNN,” Multimed. Tools Appl., vol. 81, no. 29, pp. 42183–42207, 2022.
F. Ashiq et al., “CNN-based object recognition and tracking system to assist visually impaired people,” IEEE Access, vol. 10, pp. 14819–14834, 2022.
C.-W. Chang, C.-Y. Chang, and Y.-Y. Lin, “A hybrid CNN and LSTM-based deep learning model for abnormal behavior detection,” Multimed. Tools Appl., vol. 81, no. 9, pp. 11825–11843, 2022.
A. Kurani, P. Doshi, A. Vakharia, and M. Shah, “A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting,” Ann. Data Sci., vol. 10, no. 1, pp. 183–208, 2023.
S. Talwar, S. Srivastava, M. Sakashita, N. Islam, and A. Dhir, “Personality and travel intentions during and after the COVID-19 pandemic: An artificial neural network (ANN) approach,” J. Bus. Res., vol. 142, pp. 400–411, 2022.
P. S. Kulkarni, S. N. Londhe, and M. C. Deo, “Journal of Soft Computing in Civil Engineering Artificial Neural Networks for Construction Management: A Review ARTICLE INFO ABSTRACT,” J. Soft Comput. Civ. Eng., vol. 1, no. 2, p. 70, 2017, [Online]. Available: http://creativecommons.org/licenses/by/4.0/.
A. Chattopadhyay and M. Maitra, “MRI-based brain tumor image detection using CNN based deep learning method,” Neurosci. Informatics, p. 100060, 2022.
H. Bouzidi, H. Ouarnoughi, S. Niar, and A. A. El Cadi, “Performance Modeling of Computer Vision-based CNN on Edge GPUs,” ACM Trans. Embed. Comput. Syst., vol. 21, no. 5, pp. 1–33, 2022.
W. Zhou, H. Wang, and Z. Wan, “Ore image classification based on improved CNN,” Comput. Electr. Eng., vol. 99, p. 107819, 2022.
S. Majid, F. Alenezi, S. Masood, M. Ahmad, E. S. Gündüz, and K. Polat, “Attention based CNN model for fire detection and localization in real-world images,” Expert Syst. Appl., vol. 189, p. 116114, 2022.
E. M. Normando et al., “A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells),” Expert Rev. Mol. Diagn., vol. 20, no. 7, pp. 737–748, 2020.
S. Zhong, S. Fu, and L. Lin, “A novel gas turbine fault diagnosis method based on transfer learning with CNN,” Measurement, vol. 137, pp. 435–453, 2019.
L. Deng, Y. Wang, Y. Liu, F. Wang, S. Li, and J. Liu, “A CNN-based vortex identification method,” J. Vis., vol. 22, pp. 65–78, 2019.
H. Ma, Y. Liu, Y. Ren, D. Wang, L. Yu, and J. Yu, “Improved CNN classification method for groups of buildings damaged by earthquake, based on high resolution remote sensing images,” Remote Sens., vol. 12, no. 2, p. 260, 2020.
J. Chu, J. Cai, H. Song, Y. Zhang, and L. Wei, “A novel bilinear feature and multi-layer fused convolutional neural network for tactile shape recognition,” Sensors (Switzerland), vol. 20, no. 20, pp. 1–14, 2020, doi: 10.3390/s20205822.
etric assessment of 2020,” Food Policy, vol. 107, p. 102204, 2022, https://doi.org/10.1016/j.foodpol.2021.102204.
F. Samara, B. A. Abu-Nabah, W. El-Damaty, and M. Al Bardan, “Assessment of the impact of the human Coronavirus (COVID-19) lockdown on the energy sector: a case study of Sharjah, UAE,” Energies, vol. 15, no. 4, p. 1496, 2022, https://doi.org/10.3390/en15041496.
C. Zheng, W. Shao, X. Chen, B. Zhang, G. Wang, and W. Zhang, “Real-world effectiveness of COVID-19 vaccines: a literature review and meta-analysis,” Int. J. Infect. Dis., vol. 114, pp. 252–260, 2022, https://doi.org/10.1016/j.ijid.2021.11.009.
A. Bhargava, A. Bansal, and V. Goyal, “Machine learning-based automatic detection of novel coronavirus (COVID-19) disease,” Multimed. Tools Appl., vol. 81, no. 10, pp. 13731–13750, 2022, https://doi.org/10.1007/s11042-022-12508-9.
O. J. Watson, G. Barnsley, J. Toor, A. B. Hogan, P. Winskill, and A. C. Ghani, “Global impact of the first year of COVID-19 vaccination: a mathematical modelling study,” Lancet Infect. Dis., vol. 22, no. 9, pp. 1293–1302, 2022, https://doi.org/10.1016/S1473-3099(22)00320-6.
M. Z. Rahman et al., “Face masks to combat coronavirus (covid-19)—processing, roles, requirements, efficacy, risk and Sustainability,” Polymers (Basel)., vol. 14, no. 7, p. 1296, 2022, https://doi.org/10.3390/polym14071296.
R. C. Woodruff et al., “Risk factors for severe COVID-19 in children,” Pediatrics, vol. 149, no. 1, p. e2021053418, 2022, https://doi.org/10.1542/peds.2021-053418.
I. B. M. Gandamayu, N. W. S. Antari, and I. A. S. Strisanti, “The level of community compliance in implementing health protocols to prevent the spread of COVID-19,” Int. J. Heal. Med. Sci., vol. 5, no. 2, pp. 177–182, 2022, DOI: 10.21744/ijhms.v5n2.1897.
J. Nie, L. Kang, Y. Pian, and J. Hu, “The need for more robust research on the effectiveness of masks in preventing COVID-19 transmission,” Future Virol., vol. 17, no. 7, pp. 491–494, 2022, https://doi.org/10.2217/fvl-2021-0032.
X. Ma, X.-F. Luo, L. Li, Y. Li, and G.-Q. Sun, “The influence of mask use on the spread of COVID-19 during pandemic in New York City,” Results Phys., vol. 34, p. 105224, 2022, https://doi.org/10.1016/j.rinp.2022.105224.
M. A. S. Ai et al., “Real-time facemask detection for preventing COVID-19 spread using transfer learning based deep neural network,” Electronics, vol. 11, no. 14, p. 2250, 2022, https://doi.org/10.3390/electronics11142250.
M. Fakih, S. Oktaviana, E. Nurlaili, D. Febrianto, and N. Nargis, “The Implementation Of Health Protocols On E-Court Systems During The Covid-19 Pandemic Era (In Bandar Lampung City),” Cepalo, vol. 6, no. 1, pp. 23–36, 2022, https://doi.org/10.25041/cepalo.v6no1.2584.
Z. Levine and D. J. D. Earn, “Face masking and COVID-19: potential effects of variolation on transmission dynamics,” J. R. Soc. Interface, vol. 19, no. 190, p. 20210781, 2022, https://doi.org/10.1098/rsif.2021.0781.
T. A. Kumar, R. Rajmohan, M. Pavithra, S. A. Ajagbe, R. Hodhod, and T. Gaber, “Automatic face mask detection system in public transportation in smart cities using IoT and deep learning,” Electronics, vol. 11, no. 6, p. 904, 2022, https://doi.org/10.3390/electronics11060904.
X. Du‐Harpur, F. M. Watt, N. M. Luscombe, and M. D. Lynch, “What is AI? Applications of artificial intelligence to dermatology,” Br. J. Dermatol., vol. 183, no. 3, pp. 423–430, 2020, https://doi.org/10.1111/bjd.18880.
D. Nahavandi, R. Alizadehsani, A. Khosravi, and U. R. Acharya, “Application of artificial intelligence in wearable devices: Opportunities and challenges,” Comput. Methods Programs Biomed., vol. 213, p. 106541, 2022, https://doi.org/10.1016/j.cmpb.2021.106541.
A. Alam et al., “Object Detection Learning for Intelligent Self Automated Vehicles.,” Intell. Autom. Soft Comput., vol. 34, no. 2, 2022, https://doi.org/10.32604/iasc.2022.024840.
S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. Asghar, and B. Lee, “A survey of modern deep learning based object detection models,” Digit. Signal Process., p. 103514, 2022, https://doi.org/10.1016/j.dsp.2022.103514.
Y. Chen, Y. Li, X. Zhang, J. Sun, and J. Jia, “Focal sparse convolutional networks for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5428–5437, https://doi.org/10.1109/cvpr52688.2022.00535.
C. Liu, S. ME Sepasgozar, S. Shirowzhan, and G. Mohammadi, “Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms,” Constr. Innov., vol. 22, no. 1, pp. 141–159, 2022, https://doi.org/10.1108/CI-02-2020-0017.
P. Wu, A. Liu, J. Fu, X. Ye, and Y. Zhao, “Autonomous surface crack identification of concrete structures based on an improved one-stage object detection algorithm,” Eng. Struct., vol. 272, p. 114962, 2022, https://doi.org/10.1016/j.engstruct.2022.114962.
R. Karthika and L. Parameswaran, “A novel convolutional neural network based architecture for object detection and recognition with an application to traffic sign recognition from road scenes,” Pattern Recognit. Image Anal., vol. 32, no. 2, pp. 351–362, 2022, https://doi.org/10.1134/S1054661822020110.
P. Mittal, R. Singh, and A. Sharma, “Deep learning-based object detection in low-altitude UAV datasets: A survey,” Image Vis. Comput., vol. 104, p. 104046, 2020, https://doi.org/10.1016/j.imavis.2020.104046.
Z.-Q. Zhao, P. Zheng, S. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE Trans. neural networks Learn. Syst., vol. 30, no. 11, pp. 3212–3232, 2019, DOI: 10.1109/TNNLS.2018.2876865.
T. Sharma, B. Debaque, N. Duclos, A. Chehri, B. Kinder, and P. Fortier, “Deep learning-based object detection and scene perception under bad weather conditions,” Electronics, vol. 11, no. 4, p. 563, 2022, https://doi.org/10.3390/electronics11040563.
N. Zeng, P. Wu, Z. Wang, H. Li, W. Liu, and X. Liu, “A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection,” IEEE Trans. Instrum. Meas., vol. 71, pp. 1–14, 2022, DOI: 10.1109/TIM.2022.3153997.
Y. Gong, J. Luo, H. Shao, and Z. Li, “A transfer learning object detection model for defects detection in X-ray images of spacecraft composite structures,” Compos. Struct., vol. 284, p. 115136, 2022, https://doi.org/10.1016/j.compstruct.2021.115136.
S. Rani, D. Ghai, and S. Kumar, “Object detection and recognition using contour based edge detection and fast R-CNN,” Multimed. Tools Appl., vol. 81, no. 29, pp. 42183–42207, 2022, https://doi.org/10.1007/s11042-021-11446-2.
F. Ashiq et al., “CNN-based object recognition and tracking system to assist visually impaired people,” IEEE Access, vol. 10, pp. 14819–14834, 2022, DOI: 10.1109/ACCESS.2022.3148036.
C.-W. Chang, C.-Y. Chang, and Y.-Y. Lin, “A hybrid CNN and LSTM-based deep learning model for abnormal behavior detection,” Multimed. Tools Appl., vol. 81, no. 9, pp. 11825–11843, 2022, https://doi.org/10.1007/s11042-021-11887-9.
A. Kurani, P. Doshi, A. Vakharia, and M. Shah, “A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting,” Ann. Data Sci., vol. 10, no. 1, pp. 183–208, 2023, 183–208 (2023). https://doi.org/10.1007/s40745-021-00344-x.
S. Talwar, S. Srivastava, M. Sakashita, N. Islam, and A. Dhir, “Personality and travel intentions during and after the COVID-19 pandemic: An artificial neural network (ANN) approach,” J. Bus. Res., vol. 142, pp. 400–411, 2022, https://doi.org/10.1016/j.jbusres.2021.12.002.
P. S. Kulkarni, S. N. Londhe, and M. C. Deo, “Journal of Soft Computing in Civil Engineering Artificial Neural Networks for Construction Management: A Review ARTICLE INFO ABSTRACT,” J. Soft Comput. Civ. Eng., vol. 1, no. 2, p. 70, 2017, DOI: 10.22115/SCCE.2017.49580.
A. Chattopadhyay and M. Maitra, “MRI-based brain tumor image detection using CNN based deep learning method,” Neurosci. Informatics, p. 100060, 2022, https://doi.org/10.1016/j.neuri.2022.100060.
H. Bouzidi, H. Ouarnoughi, S. Niar, and A. A. El Cadi, “Performance Modeling of Computer Vision-based CNN on Edge GPUs,” ACM Trans. Embed. Comput. Syst., vol. 21, no. 5, pp. 1–33, 2022, https://doi.org/10.1145/3527169.
W. Zhou, H. Wang, and Z. Wan, “Ore image classification based on improved CNN,” Comput. Electr. Eng., vol. 99, p. 107819, 2022, https://doi.org/10.1016/j.compeleceng.2022.107819.
S. Majid, F. Alenezi, S. Masood, M. Ahmad, E. S. Gündüz, and K. Polat, “Attention based CNN model for fire detection and localization in real-world images,” Expert Syst. Appl., vol. 189, p. 116114, 2022, https://doi.org/10.1016/j.eswa.2021.116114.
E. M. Normando et al., “A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells),” Expert Rev. Mol. Diagn., vol. 20, no. 7, pp. 737–748, 2020, https://doi.org/10.1080/14737159.2020.1758067.
S. Zhong, S. Fu, and L. Lin, “A novel gas turbine fault diagnosis method based on transfer learning with CNN,” Measurement, vol. 137, pp. 435–453, 2019, https://doi.org/10.1016/j.measurement.2019.01.022.
L. Deng, Y. Wang, Y. Liu, F. Wang, S. Li, and J. Liu, “A CNN-based vortex identification method,” J. Vis., vol. 22, pp. 65–78, 2019, https://doi.org/10.1007/s12650-018-0523-1.
H. Ma, Y. Liu, Y. Ren, D. Wang, L. Yu, and J. Yu, “Improved CNN classification method for groups of buildings damaged by earthquake, based on high resolution remote sensing images,” Remote Sens., vol. 12, no. 2, p. 260, 2020, https://doi.org/10.3390/rs12020260.
J. Chu, J. Cai, H. Song, Y. Zhang, and L. Wei, “A novel bilinear feature and multi-layer fused convolutional neural network for tactile shape recognition,” Sensors (Switzerland), vol. 20, no. 20, pp. 1–14, 2020, doi: 10.3390/s20205822
Copyright (c) 2023 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
