Browse > Article
http://dx.doi.org/10.9708/jksci.2021.26.05.001

Adaptive Face Mask Detection System based on Scene Complexity Analysis  

Kang, Jaeyong (Dept. of Software, Korea National University of Transportation)
Gwak, Jeonghwan (Dept. of Software, Korea National University of Transportation)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.5, 2021 , pp. 1-8 More about this Journal
Abstract
Coronavirus disease 2019 (COVID-19) has affected the world seriously. Every person is required for wearing a mask properly in a public area to prevent spreading the virus. However, many people are not wearing a mask properly. In this paper, we propose an efficient mask detection system. In our proposed system, we first detect the faces of input images using YOLOv5 and classify them as the one of three scene complexity classes (Simple, Moderate, and Complex) based on the number of detected faces. After that, the image is fed into the Faster-RCNN with the one of three ResNet (ResNet-18, 50, and 101) as backbone network depending on the scene complexity for detecting the face area and identifying whether the person is wearing the mask properly or not. We evaluated our proposed system using public mask detection datasets. The results show that our proposed system outperforms other models.
Keywords
Artificial intelligence; Machine learning; Object detection; Deep learning; Mask detection; COVID-19;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Krizhevsky, I. Sutskever, and G.E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, Vol. 25, pp. 1097-1105, 2012.
2 O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and L. Fei-Fei, "Imagenet large scale visual recognition challenge," International Journal of Computer Vision, Vol. 115, pp. 211-252, 2015.   DOI
3 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, and A. Rabinovich, "Going deeper with convolutions," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.
4 K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv:1409.1556, Sep, 2014.
5 K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
6 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
7 P. Viola, and M. Jones, "Rapid object detection using a boosted cascade of simple features," In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Vol. 1, pp. I-I, IEEE, 2001.
8 R. Girshick, "Fast R-CNN," In Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.
9 S.J. Pan, and Q. Yang, "A survey on transfer learning," IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 10, pp. 1345-1359, 2009.   DOI
10 Mask dataset, https://makeml.app/datasets/mask.
11 S. Akcay, M.E. Kundegorski, M. Devereux, and T.P. Breckon, "Transfer learning using convolutional neural networks for object classification within x-ray baggage security imagery," In 2016 IEEE International Conference on Image Processing (ICIP), pp. 1057-1061, IEEE, 2016.
12 I.M. Baltruschat, H. Nickisch, M. Grass, T. Knopp, and A. Saalbach, "Comparison of deep learning approaches for multi-label chest X-ray classification," Scientific Reports, Vol. 9, No. 1, pp. 1-10, 2019.   DOI
13 R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014.
14 N. Tajbakhsh, J.Y. Shin, S.R. Gurudu, R.T. Hurst, C.B. Kendall, M.B. Gotway, and J. Liang, "Convolutional neural networks for medical image analysis: Full training or fine tuning?," IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp. 1299-1312, 2016.   DOI
15 S. Christodoulidis, M. Anthimopoulos, L. Ebner, A. Christe, and S. Mougiakakou, "Multisource transfer learning with convolutional neural networks for lung pattern analysis," IEEE Journal of Biomedical and Health Informatics, Vol. 21, No. 1, pp. 76-84, 2016.   DOI
16 J. Kang, and J. Gwak, "Ensemble of instance segmentation models for polyp segmentation in colonoscopy images," IEEE Access, Vol. 7, pp. 26440-26447, 2019.   DOI
17 H. Yang, S. Mei, K. Song, B. Tao, and Z. Yin, "Transfer-learning-based online Mura defect classification," IEEE Transactions on Semiconductor Manufacturing, Vol. 31, No. 1 pp. 116-123, 2018.   DOI