Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX

Ahmad Aljaafreh, Ahmad Abadleh, Saqer S. Alja'Afreh, Khaled Alawasa, Eqab Almajali, Hossam Faris


This paper proposes a fully automated vision-based system for real-time COVID-19 personal protective equipment detection and monitoring. Through this paper, we aim to enhance the capability of on-edge real-time face mask detection as well as improve social distancing monitoring from real-live digital videos. Using deep neural networks, researchers have developed a state-of-the-art object detector called "You Only Look Once Version Five" (YOLO5). On real images of people wearing COVID19 masks collected from Google Dataset Search, YOLOv5s, the smallest variant of the object detection model, is trained and implemented. It was found that the Yolov5s model is capable of extracting rich features from images and detecting the face mask with a high precision of better than 0.88 mAP_0.5. This model is combined with the Density-Based Spatial Clustering of Applications with Noise method in order to detect patterns in the data to monitor social distances between people. The system is programmed in Python and implemented on the NVIDIA Jetson Xavier board. It achieved a speed of more than 12 frames per second.


Doi: 10.28991/ESJ-2023-SPER-05

Full Text: PDF


COVID-19; Mask Detection; Social Distancing; YOLOv5s.


Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition. ACM Computing Surveys, 35(4), 399–458. doi:10.1145/954339.954342.

Li, S. Z., & Jain, A. K. (2011). Handbook of Face Recognition. Springer-Verlag London Limited, London, United Kingdom. doi:10.1007/978-0-85729-932-1.

Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2015.7298682.

Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep Face Recognition. Procedings of the British Machine Vision Conference 2015. doi:10.5244/c.29.41.

Zhang, X., Yang, Y.-H., Han, Z., Wang, H., & Gao, C. (2013). Object class detection. ACM Computing Surveys, 46(1), 1–53. doi:10.1145/2522968.2522978.

Abudarham, N., Shkiller, L., & Yovel, G. (2019). Critical features for face recognition. Cognition, 182, 73–83. doi:10.1016/j.cognition.2018.09.002.

Rajani Kumari, L.V., Saher Fathima, S., Sai Praneeth, G., Mamatha, D., Pranitha, B. (2022). Dynamic Face Recognition System Using Histogram of Oriented Gradients and Deep Neural Network. Sustainable Communication Networks and Application. Lecture Notes on Data Engineering and Communications Technologies, 93, Springer, Singapore. doi:10.1007/978-981-16-6605-6_16.

Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), 448-456, 6-11 July, 2015, Lille, France.

Sun, K., Xiao, B., Liu, D., & Wang, J. (2019). Deep High-Resolution Representation Learning for Human Pose Estimation. CVPR 2019. Available online: https://jingdongwang2017.github.io/Projects/HRNet/ObjectDetection.html (accessed on June 2022).

Rouxel, F., Yauy, K., Boursier, G., Gatinois, V., Barat-Houari, M., Sanchez, E., Lacombe, D., Arpin, S., Giuliano, F., Haye, D., Rio, M., Toutain, A., Dieterich, K., Brischoux-Boucher, E., Julia, S., Nizon, M., Afenjar, A., Keren, B., Jacquette, A., … Genevieve, D. (2022). Using deep-neural-network-driven facial recognition to identify distinct Kabuki syndrome 1 and 2 gestalt. European Journal of Human Genetics, 30(6), 682–686. doi:10.1038/s41431-021-00994-8.

John Hopkins Universoty (JHU). COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Available online: https://coronavirus.jhu.edu/map (accessed on April 2022).

Jiang, X., Gao, T., Zhu, Z., & Zhao, Y. (2021). Real-time face mask detection method based on YOLOv3. Electronics (Switzerland), 10(7), 837. doi:10.3390/electronics10070837.

Eikenberry, S. E., Mancuso, M., Iboi, E., Phan, T., Eikenberry, K., Kuang, Y., Kostelich, E., & Gumel, A. B. (2020). To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infectious Disease Modelling, 5, 293–308. doi:10.1016/j.idm.2020.04.001.

Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience, 2018, 1–13. Doi:10.1155/2018/7068349.

Jiao, L., Zhang, R., Liu, F., Yang, S., Hou, B., Li, L., & Tang, X. (2022). New Generation Deep Learning for Video Object Detection: A Survey. IEEE Transactions on Neural Networks and Learning Systems, 33(8), 3195–3215. doi:10.1109/tnnls.2021.3053249.

Suresh, K., Palangappa, M., & Bhuvan, S. (2021). Face Mask Detection by using Optimistic Convolutional Neural Network. 2021 6th International Conference on Inventive Computation Technologies (ICICT). doi:10.1109/icict50816.2021.9358653.

Crespo, F., Crespo, A., Sierra-Martínez, L. M., Peluffo-Ordóñez, D. H., & Morocho-Cayamcela, M. E. (2022). A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness. Applied Sciences, 12(14), 6924. doi:10.3390/app12146924.

Wu, P., Li, H., Zeng, N., & Li, F. (2022). FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public. Image and Vision Computing, 117, 104341. doi:10.1016/j.imavis.2021.104341.

Vibhuti, Jindal, N., Singh, H., & Rana, P. S. (2022). Face mask detection in COVID-19: a strategic review. Multimedia Tools and Applications. doi:10.1007/s11042-022-12999-6.

Shinde, R. K., Alam, M. S., Park, S. G., Park, S. M., & Kim, N. (2022). Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model. Healthcare (Switzerland), 10(3). doi:10.3390/healthcare10030454.

Razavi, M., Alikhani, H., Janfaza, V., Sadeghi, B., & Alikhani, E. (2022). An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic. SN Computer Science, 3(1), 1–8. doi:10.1007/s42979-021-00894-0.

Github (2022). YOLOv5. GitHub, Inc. Available Online: https://github.com/ultralytics/YOLOv5 (accessed on June 2022).

Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition, 7263-7271, 21-26 July, 2017, Honolulu, United States.

Du, J. (2018). Understanding of Object Detection Based on CNN Family and YOLO. Journal of Physics: Conference Series, 1004, 012029. doi:10.1088/1742-6596/1004/1/012029.

Rajput, M. (2020). YOLO V5 is Here! Custom Object Detection Tutorial with YOLO V5. Available Online: https://towardsai.net/p/data-science/YOLO-v5-is-here-custom-object-detection-tutorial-with-YOLO-v5 (accessed on July 2022).

Supeshala, C. (2020). YOLO v4 or YOLO v5 or PP-YOLO?. Available Online: https://towardsdatascience.com/YOLO-v4-or-YOLO-v5-or-pp-YOLO-dad8e40f7109 (accessed on June 2022).

Nelson, J., & Solawetz, J. (2020). YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS. Roboflow, Available Online: https://blog.roboflow.com/YOLOv5-is-here/ (accessed on June 2022).

NVIDIA (2022). Jeston Xavier NX Series Modules. NVIDIA Corporation. Available Online: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-xavier-nx/ (accessed on April 2022).

Kumar, A., Kalia, A., & Kalia, A. (2022). ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic. Optik, 259, 169051. doi:10.1016/j.ijleo.2022.169051.

Takimoto, H., Sato, Y., Nagano, A. J., Shimizu, K. K., & Kanagawa, A. (2021). Using a two-stage convolutional neural network to rapidly identify tiny herbivorous beetles in the field. Ecological Informatics, 66, 101466. doi:10.1016/j.ecoinf.2021.101466.

Full Text: PDF

DOI: 10.28991/ESJ-2023-SPER-05


  • There are currently no refbacks.

Copyright (c) 2022 Ahmad Abadleh, AHMAD ALjaafreh, Saqer S. Alja'Afreh, E'qab E'qab R. Almajali, Hossam Faris