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http://dx.doi.org/10.33851/JMIS.2021.8.3.159

A Mask Wearing Detection System Based on Deep Learning  

Yang, Shilong (School of Software, Nanchang Hangkong University)
Xu, Huanhuan (School of Software, Nanchang Hangkong University)
Yang, Zi-Yuan (School of Computer Science, Sichuan University)
Wang, Changkun (School of Information Engineering, Nanchang Hangkong University)
Publication Information
Journal of Multimedia Information System / v.8, no.3, 2021 , pp. 159-166 More about this Journal
Abstract
COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.
Keywords
Mask wearing detection system; Deep learning; Image detection; Video detection; Real-time detection;
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1 R. Girshick. "Fast r-cnn," in Proceedings of the IEEE International Conference on Computer Vision, Santiago, pp. 1440-1448, Dec. 2015.
2 S. J. Park and B. G. Kim. "Development of low-cost vision-based eye tracking algorithm for information augmented interactive system," Journal of Multimedia Information System, vol. 7, no. 1, pp. 11-16, 2020.   DOI
3 S. Susanto, F. A. Putra, R. Analia, and I. K. L. N. Suciningtyas, "The face mask detection of preventing the spread of COVID-19 at politeknik negeri batam," in Proceeding of the 3-rd International Conference on Applied Engineering (I CAE), pp. 1-5, 2020.
4 J. Redmon, S. Divvala, R. Girshick, et al. "You only look once: Unified, real-time object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 779-788, July 2016.
5 A. Paszke, S. Gross, F. Massa, et al. "Pytorch: An imperative style, high-performance deep learning library," Advances in neural information processing systems, vol. 32, pp. 8026-8037, 2019.
6 R. Kalman. "A new approach to linear filtering and prediction problems, " Journal of Basic Engineering, vol. 82, no. Series D, pp. 35-45, 1960.   DOI
7 Y. J. Heo, B. G. Kim, P. P. Roy, "Frontal Face Generation Algorithm from Multi-view Images Based on Generative Adversarial Network," Journal of Multimedia Information System, vol. 8, no. 2, pp. 85-92, 2019.
8 J. H. Kim, B. G. Kim, P. P. Roy, et al., "Efficient facial expression recognition algorithm based on hierarchical deep neural network structure," IEEE Access, vol.7, pp. 41273-41285, 2019.   DOI
9 Z. Yang, L. Leng and W. Min "Extreme Downsampling and Joint Feature for Coding-Based Palmprint Recognition," IEEE Transactions on Instrumentation and Measurement, vol. 70, no. 1-12, 2021.
10 Y. Zhang, J. Chu, L. Leng, et al. "Mask-refined R-CNN: A network for refining object details in instance segmentation, " Sensors, vol. 20, no. 4, pp. 1010.   DOI
11 Y. J. Heo, B. G. Kim and P. P. Roy. "Frontal face generation algorithm from multi-view images based on generative adversarial network," Journal of Multimedia Information System, vol. 8, no. 2, pp. 85-92, 2021.   DOI
12 J. Chu, Z. Guo and L. Leng. "Object detection based on multi-layer convolution feature fusion and online hard example mining," IEEE Access, vol. 6, pp. 19959-19967, 2018.   DOI
13 L. Leng, Z. Yang, C. Kim, et al. "A light-weight practical framework for feces detection and trait recognition," Sensors, vol. 20, no. 9, pp. 2644, 2020.   DOI
14 Weekly epidemiological update on COVID-19 - 20 April 2021, https://www.who.int/publications/m/item/weeklyepidemiological-update-on-covid-19---20-april-2021, 2021.
15 B. Asadi, N. Bouvier, A. S. Wexler, et al. "The coronavirus pandemic and ae-rosols: Does COVID-19 transmit via expiratory particles?" The Lancet Respiratory Medicine, vol. 8, no. 5, pp. 434-436, 2020.   DOI
16 A. Bhattacharyya, R. Saini, P. P. Roy, et al., "Recognizing gender from human facial regions using genetic algorithm," Soft Computing, vol. 23, no. 17, pp. 8085-8100, 2019.   DOI
17 L. Leng, J. Zhang, M. K. Khan, et al. "Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain," International Journal of the Physical Sciences, vol. 5, no. 17, pp. 2543-2554, 2010.
18 L. Leng, J. Zhang, G. Chen, et al. "Two-directional two-dimensional random projection and its variations for face and palmprint recognition," in Proceedings of the International Conference on Computational Science and its Applications, Berlin, pp.458-470, June 2011.
19 Z. Yang, L. Leng and B. G. Kim. "StoolNet for color classification of stool medical images," Electronics, vol. 8, no. 12, pp. 1464, 2019.   DOI
20 H. J. Kwon, G. P. Lee, Y. J. Kim, et al. "Comparison of pre-processed brain tumor MR images using deep learning detection algorithms," Journal of Multimedia Information System, vol. 8, no. 2, pp. 79-84.
21 J. H. Kim, G. S. Hong, B. G. Kim, et al., "deepGesture: Deep learning-based gesture recognition scheme using motion sensors," Displays, vol. 55, pp. 34-45, 2018.
22 W. Liu, D. Anguelov, D. Erhan, et al. "SSD: Single shot multibox detector," in Proceedings of the European Conference on Computer Vision, Amsterdam, pp. 21-37, Oct. 2016.
23 A. Bewley, Z. Ge, L. Ott, et al. "Simple online and realtime tracking," in Proceedings of the IEEE International Conference on Image Processing, Phoenix, pp. 3464-3468, Sep. 2016.
24 Z. Yang, J. Li, W. Min, et al. "Real-time pre-identification and cascaded detection for tiny faces," Applied Sciences, vol. 9, no. 20, pp. 4344, 2019.   DOI
25 J. Redmon and A. Farhadi. "Yolov3: An incremental improvement," arXiv preprint, arXiv:1804.02767, 2018.
26 Y. Yuan, J. Chu, L. Leng, et al. "A scale-adaptive object-tracking algorithm with occlusion detection," EURASIP Journal on Image and Video Processing, vol. 1, pp. 1-15, 2020.
27 S. Sethi, M. Kathuria and T. Kaushik. "Face mask detection using deep learning: An approac h to reduce risk of Coronavirus spread," Journal of Bio medical Informatics, vol. 120, pp. 103848, 2021.   DOI
28 L. Leng, S. Zhang, X. Bi, et al. "Two-dimensional cancelable biometric scheme, " in Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, Xi'an, July 2012.