Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread
Tài liệu tham khảo
World Health Organization et al. Coronavirus disease 2019 (covid-19): situation report, 96. 2020. - Google Search. (n.d.). https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200816-covid-19-sitrep-209.pdf?sfvrsn=5dde1ca2_2.
Social distancing, surveillance, and stronger health systems as keys to controlling COVID-19 Pandemic, PAHO Director says - PAHO/WHO | Pan American Health Organization. (n.d.). https://www.paho.org/en/news/2-6-2020-social-distancing-surveillance-and-stronger-health-systems-keys-controlling-covid-19.
Garcia Godoy, 2020, Facial protection for healthcare workers during pandemics: a scoping review, BMJ, Glob. Heal., 5
Eikenberry, 2020, To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic, Infect. Dis. Model., 5, 293
Wearing surgical masks in public could help slow COVID-19 pandemic’s advance: Masks may limit the spread diseases including influenza, rhinoviruses and coronaviruses -- ScienceDaily. (n.d.). https://www.sciencedaily.com/releases/2020/04/200403132345.htm.
Nanni, 2017, Handcrafted vs. non-handcrafted features for computer vision classification, Pattern Recogn., 71, 158, 10.1016/j.patcog.2017.05.025
Y. Jia et al., Caffe: Convolutional architecture for fast feature embedding, in: MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia, 2014, doi: 10.1145/2647868.2654889.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Lecun, OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, 2014.
Erhan, 2014, Scalable Object Detection using Deep Neural Networks, 2147
J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 779–788, doi: 10.1109/CVPR.2016.91.
M. Jiang, X. Fan, and H. Yan, RetinaMask: A Face Mask detector, 2020, http://arxiv.org/abs/2005.03950.
Inamdar, 2020, Real-Time Face Mask Identification Using Facemasknet Deep Learning Network, SSRN Electron. J., 10.2139/ssrn.3663305
Qiao, 2018, Few-Shot Image Recognition by Predicting Parameters from Activations
Kumar, 2020, Object detection in real time based on improved single shot multi-box detector algorithm, J. Wireless Com. Netw., 2020, 204, 10.1186/s13638-020-01826-x
Morera, 2020, SSD vs. YOLO for detection of outdoor urban advertising panels under multiple variabilities, Sensors (Switzerland), 10.3390/s20164587
Girshick, 2015, Region-based Convolutional Networks for Accurate Object Detection and Segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 38, 142, 10.1109/TPAMI.2015.2437384
He, 2015, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, IEEE Trans. Pattern Anal. Mach. Intell., 10.1109/TPAMI.2015.2389824
R. Girshick, Fast R-CNN, in: Proc. IEEE Int. Conf. Comput. Vis., vol. 2015 Inter, 2015, pp. 1440–1448, doi: 10.1109/ICCV.2015.169.
Nguyen, 2020, An Evaluation of Deep Learning Methods for Small Object Detection, J. Electr. Comput. Eng., 2020
Cai, 2016, A unified multi-scale deep convolutional neural network for fast object detection, Lect. Notes Comput. Sci. (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, A.C. Berg, DSSD : Deconvolutional Single Shot Detector, 2017, arXiv preprint arXiv:1701.06659 (2017).
A. Shrivastava, R. Sukthankar, J. Malik, A. Gupta, Beyond Skip Connections: Top-Down Modulation for Object Detection, 2016, arXiv preprint arXiv:1612.06851 (2016).
N. Dvornik, K. Shmelkov, J. Mairal, C. Schmid, BlitzNet: A Real-Time Deep Network for Scene Understanding, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, doi: 10.1109/ICCV.2017.447.
Z. Liang, J. Shao, D. Zhang, L. Gao, Small object detection using deep feature pyramid networks, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11166 LNCS, pp. 554–564, doi: 10.1007/978-3-030-00764-5_51.
K. He, G. Gkioxari, P. Dollar, R. Girshick, Mask R-CNN, in: Proc. IEEE Int. Conf. Comput. Vis., vol. 2017-Octob, 2017, pp. 2980–2988, doi: 10.1109/ICCV.2017.322.
P. Soviany, R.T. Ionescu, Optimizing the trade-off between single-stage and two-stage deep object detectors using image difficulty prediction, in: Proc. - 2018 20th Int. Symp. Symb. Numer. Algorithms Sci. Comput. SYNASC 2018, 2018, pp. 209–214, doi: 10.1109/SYNASC.2018.00041.
Lin, 2020, Focal Loss for Dense Object Detection, IEEE Trans. Pattern Anal. Mach. Intell., 10.1109/TPAMI.2018.2858826
J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li, Li Fei-Fei, ImageNet: A large-scale hierarchical image database, 2010, doi: 10.1109/cvpr.2009.5206848.
T. Y. Lin et al., Microsoft COCO: Common objects in context, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8693 LNCS, no. PART 5, pp. 740–755, doi: 10.1007/978-3-319-10602-1_48.
Apostolopoulos, 2020, Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys. Eng. Sci. Med., 10.1007/s13246-020-00865-4
V. Jain, E. Learned-Miller, Fddb: A benchmark for face detection in unconstrained settings, UMass Amherst Tech. Rep., 2010, vol. 2, no. 4, UMass Amherst technical report, 2010.
B. Yang, J. Yan, Z. Lei, S.Z. Li, Fine-grained evaluation on face detection in the wild, in: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015, 2015, doi: 10.1109/FG.2015.7163158.
Z. Liu, P. Luo, X. Wang, X. Tang, Large-scale celebfaces attributes (celeba) dataset, Retrieved August, 2018, Retrieved August, 15(2018), 11.
S. Yang, P. Luo, C.C. Loy, X. Tang, WIDER FACE: A face detection benchmark, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, doi: 10.1109/CVPR.2016.596.
MAFA (MAsked FAces) - Datasets - CKAN, http://221.228.208.41/gl/dataset/0b33a2ece1f549b18c7ff725fb50c561.
Z. Wang et al., Masked Face Recognition Dataset and Application, 2020, pp. 1–3, arXiv preprint arXiv: 2003.09093 (2020).
Roy, 2020, MOXA: A Deep Learning Based Unmanned Approach For Real-Time Monitoring of People Wearing Medical Masks, Trans. Indian Natl. Acad. Eng., 10.1007/s41403-020-00157-z
K. Chen et al., MMDetection: Open MMLab Detection Toolbox and Benchmark, 2019, arXiv preprint arXiv:1906.07155 (2019). http://bamos.github.io/2016/01/19/openface-0.2.0/.
http://bamos.github.io/2016/01/19/openface-0.2.0/.
G.J. Chowdary, N.S. Punn, S.K. Sonbhadra, S. Agarwal, Face Mask Detection using Transfer Learning of InceptionV3, in: International Conference on Big Data Analytics, Springer, Cham, 2020, pp. 81-90, pp. 1–11, doi:10.1007/978-3-030-66665-1_6.
Ionescu, 2017, How hard can it be? Estimating the difficulty of visual search in an image, 2157
Chen, 2014, Object Detection Scheme Using Cross Correlation and Affine Transformation Techniques, Int. J. Comput. Consum. Contr. (IJ3C), 3
Girshick, 2014, Rich feature hierarchies for accurate object detection and semantic segmentation
The Right Loss Function [PyTorch] | by Hmrishav Bandyopadhyay | Heartbeat.
Bianco, 2018, Benchmark analysis of representative deep neural network architectures, IEEE Access, 6, 64270, 10.1109/ACCESS.2018.2877890