Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions

Authors

  • Fachrul Kurniawan Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
  • I Nyoman Gede Arya Astawa Politeknik Negeri Bali
  • I Made Ari Dwi Suta Atmaja Politeknik Negeri Bali
  • Aji Prasetya Wibawa Universitas Negeri Malang

DOI:

https://doi.org/10.26594/register.v9i2.3249

Keywords:

face detection, mask, dataset, YOLO-v5

Abstract

The Covid-19 pandemic has made it imperative to prioritize health standards in companies and public areas with a large number of people. Typically, officers oversee the usage of masks in public spaces; however, computer vision can be employed to facilitate this process. This study focuses on the detection of facemask usage utilizing the YOLO-v5 algorithm across various datasets and resolutions. Three datasets were employed: the face with mask dataset (M dataset), the synthetic dataset (S dataset), and the combined dataset (G dataset), with image resolutions of 320 pixels and 640 pixels, respectively. The objective of this study is to assess the accuracy of the YOLO-v5 algorithm in detecting whether an individual is wearing a mask or not. In addition, the algorithm was tested on a dataset comprising individuals wearing masks and a synthetic dataset. The training results indicate that higher resolutions lead to longer training times, but yield excellent prediction outcomes. The system test results demonstrate that face image detection using the YOLO-v5 method performs exceptionally well at a resolution of 640 pixels, achieving a detection rate of 99.2 percent for the G dataset, 98.5 percent for the S dataset, and 98.9 percent for the M dataset. These test results provide evidence that the YOLO-v5 algorithm is highly recommended for accurate detection of facemask usage.

Author Biography

Fachrul Kurniawan, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia

Department of Informatics Engineering

References

Oumina, A., N.E. Makhfi, and M. Hamdi, "Control The COVID-19 Pandemic: Face Mask Detection Using Transfer Learning". in 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). 2020.

Donthu, N. and A. Gustafsson, "Effects of COVID-19 on business and research". Journal of business research, 2020. 117: p. 284-289.

Charoenpong, T., C. Nuthong, and U. Watchareeruetai, "A new method for occluded face detection from single viewpoint of head". in 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). 2014.

Khan, A.I. and S. Al-Habsi, "Machine Learning in Computer Vision". Procedia Computer Science, 2020. 167: p. 1444-1451.

Yang, G., et al., "Face Mask Recognition System with YOLOV5 Based on Image Recognition". in 2020 IEEE 6th International Conference on Computer and Communications (ICCC). 2020.

Neethu, N. and B. Anoop, "Role of Computer Vision in Automatic Inspection Systems". International Journal of Computer Applications, 2015. 123: p. 28-31.

Bhuiyan, M.R., S.A. Khushbu, and M.S. Islam, "A Deep Learning Based Assistive System to Classify COVID-19 Face Mask for Human Safety with YOLOv3". in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2020.

Qiang, B., et al., "Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images". Sensors, 2020. 20(18): p. 5080.

Murtaza, G., et al., "Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges". Artificial Intelligence Review, 2020. 53(3): p. 1655-1720.

Cheng, W.-C., H.-C. Hsiao, and D.-W. Lee, "Face recognition system with feature normalization". International Journal of Applied Science and Engineering, 2021. 18(1): p. 1-9.

Mahdi, F.P., et al., "Face recognition-based real-time system for surveillance". Intelligent Decision Technologies, 2017. 11(1): p. 79-92.

Hitoshi, I., "Video Face Recognition System Enabling Real-time Surveillance". NEC Technical Journal, 2016. 11(1): p. 36-39.

Kranthikiran, B. and P. Pulicherla, "Face Detection and Recognition for use in Campus Surveillance". International Journal of Innovative Technology and Exploring Engineering (IJITEE, 2020. 9(3).

Parmar, D.N. and B.B. Mehta, "Face Recognition Methods & Applications". International Journal Computer and Applications, 2013. 4(1): p. 84-86.

Wang, L., H. Zhang, and Z. Wang, "Component based representation for face recognition". in 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). 2015.

Singh, Y.K. and V. Hruaia, "Detecting face region in binary image". in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS). 2015.

Mekami, H. and S. Benabderrahmane, "Towards a new approach for real time face detection and normalization". in 2010 International Conference on Machine and Web Intelligence. 2010.

Khadatkar, A., R. Khedgaonkar, and K.S. Patnaik, "Occlusion invariant face recognition system". in 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave). 2016.

Srinivasan, S., et al., "COVID-19 Monitoring System using Social Distancing and Face Mask Detection on Surveillance video datasets". in 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). 2021.

Susanto, S., et al., "The Face Mask Detection For Preventing the Spread of COVID-19 at Politeknik Negeri Batam". in 2020 3rd International Conference on Applied Engineering (ICAE). 2020.

Abbasi, S., H. Abdi, and A. Ahmadi, "A Face-Mask Detection Approach based on YOLO Applied for a New Collected Dataset". in 2021 26th International Computer Conference, Computer Society of Iran (CSICC). 2021.

Sermanet;, P., et al., "OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks". arXiv:1312.6229, 2014. 4.

Srivastava, S., et al., "Comparative analysis of deep learning image detection algorithms". Journal of Big Data, 2021. 8(1): p. 66.

Redmon, J., et al., "You Only Look Once : Unified, Real-Time Object Detection", in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016. p. 779-788.

Geethapriya. S, N. Duraimurugan, and S.P. Chokkalingam, "Real-Time Object Detection with Yolo". International Journal of Engineering and Advanced Technology (IJEAT), 2019. 8(3S): p. 578-581.

Blaschko, M.B. and C.H. Lampert, "Learning to Localize Objects with Structured Output Regression". in Computer Vision – ECCV 2008. 2008. Berlin, Heidelberg: Springer Berlin Heidelberg.

Kannan, A., et al., "Face Mask Detection Using Yolo V5". International Journal of Novel Research and Development (IJNRD), 2022. 7(5): p. 390-395.

Shetty, R.U., et al., "Real Time Face Mask Detectionusing Yolov5 and OpenCv". International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022. 10(VI): p. 1925-1931.

Fauzi, A., et al., "Development of Health Mask Identification Using YOLOv5 Architecture". International Journal Of Artificial Intelegence Research, 2022. 6(1): p. 1-8.

Larxel, A., "Face Mask Detection". 2020 [cited 2021 May 27]; Available from: https://www.kaggle.com/andrewmvd/face-mask-detection.

Javed, I., et al., "Face mask detection and social distance monitoring system for COVID-19 pandemic". Multimedia Tools and Applications, 2023. 82(9): p. 14135-14152.

Muhamad, M. and T. Wan Sen, "Real-Time Detection of Face Masked & Face Shield Using YOLO Algorithm with Pre-Trained Model and Darknet". Indonesian Journal of Artificial Intelligence and Data Mining, 2021. 4(2): p. 97-107.

Downloads

Published

2023-07-28

How to Cite

[1]
F. Kurniawan, I. N. G. A. Astawa, I. M. A. D. S. Atmaja, and A. P. Wibawa, “Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions ”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 9, no. 2, pp. 95–102, Jul. 2023.

Issue

Section

Article