Object Detection Model for Web-Based Physical Distancing Detector Using Deep Learning


  • Andry Chowanda Bina Nusantara University
  • Ananda Kevin Refaldo Sariputra Bina Nusantara University
  • Ricardo Gunawan Prananto Bina Nusantara University




Object Detection, Web-based Application, Physical Distancing Detector, Deep Learning


The pandemic has changed the way people interact with each other in the public setting. As a result, social distancing has been implemented in public society to reduce the virus’s spread. Automatically detecting social distancing is paramount in reducing menial manual tasks. There are several methods to detect social distance in public, and one is through a surveillance camera. However, detecting social distance through a camera is not an easy task. Problems, such as lighting, occlusion, and camera resolution, can occur during detection. The research aims to develop a physical distancing detector system that is adjusted to work with Indonesian rules and conditions, especially in Jakarta, using deep learning (i.e., YOLOv4 architecture with the Darknet framework) and the CrowdHuman dataset. The detection is done by reading the source video, detecting the distance between individuals, and determining the crowd of individuals close to each other. In order to accomplish the detection, the training is done with CSPDarknet53 and VGG16 backbone in YOLOv4 and YOLOv4 Tiny architecture using various hyperparameters in the training process. Several explorations are made in the research to find the best combination of architectures and fine-tune them. The research successfully detects crowds at the 16th training, with mAP50 of 71.59% (74.04% AP50) and 16.2 Frame per Second (FPS) displayed on the web. The input size is essential for determining the model’s accuracy and speed. The model can be implemented in a web-based application.


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Author Biographies

Andry Chowanda, Bina Nusantara University

Computer Science Department, School of Computer Science

Ananda Kevin Refaldo Sariputra, Bina Nusantara University

Computer Science Department, School of Computer Science

Ricardo Gunawan Prananto, Bina Nusantara University

Computer Science Department, School of Computer Science


Z. Allam, “The first 50 days of COVID-19: A detailed chronological timeline and extensive review of literature documenting the pandemic,” Surveying the Covid-19 Pandemic and Its Implications, vol. 2020, pp. 1–7, 2020.

World Health Organization, “Naming the coronavirus disease (COVID-19) and the virus that causes it,” 2020. [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019) -and-the-virus-that-causes-it

S. Galea, R. M. Merchant, and N. Lurie, “The mental health consequences of COVID-19 and physical distancing: The need for prevention and early intervention,” JAMA Internal Medicine, vol. 180, no. 6, pp. 817–818, 2020.

S. Matta, S. Rajpal, K. K. Chopra, and V. K. Arora, “COVID-19 vaccines and new mutant strains impacting the pandemic,” The Indian Journal of Tuberculosis, vol. 68, no. 2, pp. 171–173, 2021.

R. Fadhli, “Mengenal protokol kesehatan 5M untuk cegah COVID-19,” 2023. [Online]. Available: https://tinyurl.com/4cm7v9ek

A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” pp. 1–17, 2020. [Online]. Available: https://arxiv.org/abs/2004.10934

K. B. Chethan, R. Punitha, and Mohana, “YOLOv3 and YOLOv4: Multiple object detection for surveillance applications,” in 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, Aug. 20–22, 2020, pp. 1316–1321.

Z. Zhang, S. Qiao, C. Xie, W. Shen, B. Wang, and A. L. Yuille, “Single-shot object detection with enriched semantic,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, June 18–22, 2018, pp. 5813–5821.

K. H. Shih, C. T. Chiu, J. A. Lin, and Y. Y. Bu, “Real-time object detection with reduced region proposal network via multi-feature concatenation,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 6, pp. 2164–2173, 2020.

I. H. Sarker, “Machine learning: Algorithms, realworld applications and research direction,” SN Computer Science, vol. 2, pp. 1–21, 2021.

Y. Liu, P. Sun, N. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Systems with Applications, vol. 172, 2021.

J. Redmon, “Darknet: Open source neural networks in C,” 2013–2016. [Online]. Available: http://pjreddie.com/darknet/

J. Li and Z. Wu, “The application of YOLOv4 and a new pedestrian clustering algorithm to implement social distance monitoring during the COVID-19 pandemic,” Journal of Physics: Conference Series, vol. 1865, pp. 1–15, 2021.

D. Yang, E. Yurtsever, V. Renganathan, K. A. Redmill, and U¨ . O¨ zgu¨ner, “A vision-based social distancing and critical density detection system for COVID-19,” Sensors, vol. 21, no. 13, pp. 1–15, 2021.

J. Walsh, O. Kesa, A. Wang, M. Ilas, P. O’Hara, O. Giles, N. Dhir, M. Girolami, and T. Damoulas, “Near real-time social distance estimation in London,” The Computer Journal, vol. 67, no. 1, pp. 95–109, 2024.

M. Razavi, H. Alikhani, V. Janfaza, B. Sadeghi, and E. Alikhani, “An automatic system to monitor the physical distance and face mask wearing of construction workers in COVID-19 pandemic,” SN Computer Science, vol. 3, pp. 1–8, 2022.

M. Kamari and Y. Ham, “AI-based risk assessment for construction site disaster preparedness through deep learning-based digital twinning,” Automation in Construction, vol. 134, 2022.

S. Park, I. C. Michelow, and Y. J. Choe, “Shifting patterns of respiratory virus activity following social distancing measures for coronavirus disease 2019 in South Korea,” The Journal of Infectious Diseases, vol. 224, no. 11, pp. 1900–1906, 2021.

O. Karaman, A. Alhudhaif, and K. Polat, “Development of smart camera systems based on artificial intelligence network for social distance detection to fight against COVID-19,” Applied Soft Computing, vol. 110, pp. 1–11, 2021.

M. A. Ansari and D. K. Singh, “Monitoring social distancing through human detection for preventing/reducing COVID spread,” International Journal of Information Technology, vol. 13, no. 3, pp. 1255–1264, 2021.

S. Shao, Z. Zhao, B. Li, T. Xiao, G. Yu, X. Zhang, and J. Sun, “CrowdHuman: A benchmark for detecting human in a crowd,” 2018. [Online]. Available: https://arxiv.org/abs/1805.00123

L. Tan, T. Huangfu, L. Wu, and W. Chen, “Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification,” BMC Medical Informatics and Decision Making, vol. 21, pp. 1–11, 2021.



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