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Mask detection algorithm based on pyramid box

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DOI: 10.23977/jipta.2020.31002 | Downloads: 5 | Views: 281

Author(s)

Yueyuan Liu 1, Jun Liu 1, Kai Chen 1

Affiliation(s)

1 College of information science and technology, Chengdu University of Technology (College of network security, Oxford Brooks College), Chengdu, 610059

Corresponding Author

Yueyuan Liu

ABSTRACT

The novel coronavirus outbreak there was no parallel in history. In early 2020, the outbreak of an unprecedented coronavirus outbreak could effectively block the spread of the epidemic in the crowd in the public areas of the society if it could use artificial intelligence technology to detect people without masks in the crowded area. This paper introduces the face mask image detection algorithm, mainly based on the face recognition algorithm pyramid box to complete, describes how to use Baidu's paddlehub mask detection project, through the use of Python language to complete the mask detection of multiple images and get accurate detection data. The accuracy rate of face detection based on the pyramid box mask detection model is not satisfactory. It can also be deployed to the server or even the mobile terminal to achieve rapid real-time detection. With the resumption of work of enterprises, I believe that the face mask detection scheme can solve many pain points that need to be solved for many enterprises, communities and manufacturers.

KEYWORDS

PyramidBox algorithm, Deep learning, Convolutional neural network, Face mask image detection

CITE THIS PAPER

Yueyuan Liu, Jun Liu and Kai Chen, Mask detection algorithm based on pyramid box. Journal of Image Processing Theory and Applications (2020) Vol. 3: 11-19. DOI: http://dx.doi.org/10.23977/jipta.2020.31002.

REFERENCES

[1] Zhai Junhai, Zhang Sufang, Hao Pu. Convolutional neural network and its research progress [J]. Journal of Hebei University (NATURAL SCIENCE EDITION) (06): 85-96
[2] Chang Liang, Deng Xiaoming, Zhou Mingquan, et al. Convolutional neural network in image understanding [J]. Acta automatica Sinica, 2016, 42 (9)
[3] Wan shining. Research and implementation of face recognition based on convolutional neural network [D]. University of Electronic Science and technology, 2016
[4] Lu Hongtao, Zhang Qinchuan. A review of the application of deep convolution neural network in computer vision [J]. Data acquisition and processing (1): 1-17, total 17 pages
[5] Ke Xiaolong. Application of convolution neural network in image classification [D]. Shenzhen University
[6] Su Yue. Research and analysis of image recognition technology based on deep learning convolutional neural network [J]. Information and communication, 2019 (7)
[7] Gao Fei, Jiang Jianguo, an Hongxin, et al. A fast moving target detection algorithm [C] // abstracts of the 22nd National Conference on computer technology and application (cacis · 2011) and the 3rd National Conference on key security technologies and Applications (SCA · 2011). 2011
[8] Li Xudong, Ye Mao, Li Tao. A review of target detection based on convolutional neural network [J]. Computer Application Research (10): 7-12 + 17
[9] Huang Z. research on target detection model based on convolutional neural network [D]. Shanghai Jiaotong University
[10] Zhang zemiao, Huo Huan, Zhao Fengyu. A survey of target detection algorithms based on deep convolution neural network [J]. Minicomputer system, 2019, 40 (9)
11] Li Yandong, Hao Zongbo, Lei hang. A review of convolutional neural networks [J]. Computer applications (9): 2508-2515
[12] Zhou Feiyan, Jin Linpeng, Dong Jun. a review of convolutional neural networks [J]. Acta Sinica Sinica (6)
[13] Li Feiteng. Convolutional neural network and its application [D]. Dalian University of technology, 2014
[14] Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (6): 1137-1149.
[15] Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector [J]. 2016.
[16] Zhang S, Zhu X, Lei Z, et al. S^3FD: Single Shot Scale-invariant Face Detector [J]. 2017.
[17] Lin T Y, Dollár, Piotr, Girshick R, et al. Feature Pyramid Networks for Object Detection [J]. 2016.
[18] Tang X, Du D K, He Z, et al. PyramidBox: A Context-assisted Single Shot Face Detector [J]. 2018.

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