A Comparative Study of Embedded Learning Models IoT-Based For Real time Mask Detection
Abstract
Following the outbreak of the coronavirus, many preventive measures are implemented to slow down the transmission of the virus. Amongst others, face mask detection is a key innovative technology that allows the identification of the number of individuals wearing face masks. In this regard, this paper provides a comparative study of several machine learning and deep learning algorithms (e.g., SVM, RNN, Mask-RCNN, LSTM, CNN, Auto-Encoder, GAN, U-Net GAN) that support mask detection
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[5] Suparna Biswas, Senjuti Mazumdar, Sangeeta Rana, S.B. Amreen Saba, and al. Face detection based approach to combat with COVID-19. IOP Publishing, 1797(1), feb 2021.
[6] Wei Bu, Jiangjian Xiao, Chuanhong Zhou, Minmin Yang, and Chengbin Peng. A cascade framework for masked face detection. In IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM),
2017.
[7] Adnane Cabani, Karim Hammoudi, Halim Benhabiles, and Mahmoud Melkemi. Maskedface-net - a dataset of correctly/incorrectly masked face images in the context of covid-19. Smart Health, 19, 2021.
[8] A. Chavda, J. Dsouza, S. Badgujar, , and A. Damani. Multi-stage cnn architecture for face mask detection. In 6th International Conference for Convergence in Technology (I2CT), 2020.
[9] Arjya Das, Mohammad Wasif Ansari, and Rohini Basak. Covid-19 face mask detection using tensorflow, keras and opencv. In IEEE India Council International Conference (INDICON), pages 1–5, 2020.
[10] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, and al. Generative adversarial networks. Communications of the ACM, 63, 2020.
[11] Sandeep Gupta, S.V.N. Sreenivasu, Kuldeep Chouhan, and al. Novel face mask detection technique using machine learning to control covid’19 pandemic. Materials Today: Proceedings, 2021.
[12] Kaiming He, Georgia Gkioxari, Piotr Doll´ar, and Ross Girshick. Mask r-cnn. In IEEE International Conference on Computer Vision (ICCV),
2017.
[13] D.P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR),
2014.
[14] Tsung-Yi Lin, Michael Maire, Serge Belongie, and al. Microsoft coco: Common objects in context. In European Conference on Computer Vision (ECCV), 2014.
[15] Mohamed Loey, Gunasekaran Manogaran, Mohamed Hamed N. Taha, and al. Fighting against covid-19: A novel deep learning model based
on yolo-v2 with resnet-50 for medical face mask detection. Sustainable Cities and Society, 65, 2021.
[16] Mohamed Loey, Gunasekaran Manogaran, Mohamed Hamed N. Taha, and al. A hybrid deep transfer learning model with machine learning
methods for face mask detection in the era of the covid-19 pandemic. Measurement, 167, 2021.
[17] Afsana Nowrin, Sharmin Afroz, MD. Sazzadur Rahman, and al. Comprehensive review on facemask detection techniques in the context of
covid-19. volume 29, 2020.
[18] Joseph Redmon and Ali Farhadi. YOLO9000: better, faster, stronger. CoRR, 2016.
[19] S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in Neural
Information Processing Systems (NIPS), 2015.
[20] Mark Sandler, Andrew Howard, Menglong Zhu, and al. Mobilenetv2: Inverted residuals and linear bottlenecks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[21] Samuel Ady Sanjaya and Suryo Adi Rakhmawan. Face mask detection using mobilenetv2 in the era of covid-19 pandemic. In International
Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020.
[22] Edgar Schonfeld, Bernt Schiele, and Anna Khoreva. A u-net based discriminator for generative adversarial networks. In IEEE/CVF Computer
Vision and Pattern Recognition Conference (CVPR), 2020.
[23] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A
unified embedding for face recognition and clustering. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
[24] Shay E. Snyder and Ghaith Husari. Thor: A deep learning approach for face mask detection to prevent the covid-19 pandemic. In SoutheastCon, 2021.
[25] Soham Taneja, Anand Nayyar, Vividha, and Preeti Nagrath. Face mask detection using deep learning during covid-19. In International Conference on Computing, Communications and Cyber-Security, 2021.
Authors
Meddaoui, M. A. ., Erritali, M., & Sailhan, françoise. (2023). A Comparative Study of Embedded Learning Models IoT-Based For Real time Mask Detection. International Journal of Advanced Science and Computer Applications, 3(2). https://doi.org/10.47679/ijasca.v3i2.49
This work is licensed under a Creative Commons Attribution 4.0 International License.