Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB

Muhammad Pandu Dwi Cahyo - Computer Engineering Study Program, Informatics Department, Faculty of Computer Science, Brawijaya University, Indonesia
Fitri Utaminingrum - Computer Vision Research Groups Faculty of Computer Science, Brawijaya University, Indonesia

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The prediction of COVID-19 cases will continue to experience a surge, inseparable from the presence of a new variant of the coronavirus in the world. One of the best ways to prevent transmission of the virus is to avoid or limit contact with people showing symptoms of COVID-19 or any respiratory infection. The number of medical personnel infected when interacting with patients directly also needs to be an essential concern. Hence, an autonomous robot based on room nameplate recognition systems is a solution. It can be used as an intermediary medium for medical personnel with patients to reduce the intensity of direct contact primarily can be implemented in the hospital. It is expected to reduce the spread of the COVID-19 virus, especially among health workers. Each patient room in the hospital has its room nameplate to be used as a robot reference in navigating. This research aims to make a room nameplate recognition system using the YOLOv4 method on NVIDIA Jetson Nano 2GB that produces an output for 4-wheeled robot navigation control to move. This system is designed to detect rooms within a range of 1-3 meters using 5W and 10W power modes. The testing results based on recognition is obtained an average accuracy value of 95.34%. The system performance test results based on the power mode resulted in the best average computing time of 0.149 seconds. The average value of the accuracy of output integration with the system is 94.73%.


Autonomous robot; recognition; nameplate; YOLOv4; NVIDIA Jetson Nano 2GB.

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WHO, (2020). Retrieved from WHO Director-General's opening remarks at the media briefing on COVID-19 - 11 March 2020. [online] Available at:

WHO Coronavirus (COVID-19) Dashboard, (2022). [online] Available at:

Worldometers, (2022). COVID-19 Coronavirus Pandemic. [online] Available :

J. Howard, A. Huang, Z. Li, Z. Tufecki, V. Zdimal, H.V.D. Westhuizen, A,V Delftm A. Price, L. Fridman, L.H. Tang, V. Tang, G.L. Watson, C.E. Bax, R. Shaikh, F. Questier, D. Hernandez, L.F Chu, C.M. Ramirez, A.W. Rimoin. “An evidence review of face masks against COVID-19â€, PNAS 2021 Vol. 118 No. 4 e2014564118.

Bogue R. Robots in a contagious world. Ind Rob 2020;47:642–73.

Yang G-Z, Nelson BJ, Murphy RR, Choset H, Christensen H, Collins SH, et al. Combating COVID-19-The role of robotics in managing public health and infectious diseases. Sci Robot 2020;5.

Khaleghi A, Mohammadi MR, Jahromi GP, Zarafshan H. New ways to manage pandemics: using technologies in the era of COVID-19, a narrative review. Iran J Psychiatry 2020;15:236–42.

Sharma D, Nawab AZB, Alam M. Integrating M-Health with IoMT to counter COVID-19. Stud Comput Intell 2021;923:373–96.

Khan ZH, Siddique A, Lee CW. Robotics utilization for healthcare digitization in global COVID-19 management. Int J Environ Res Public Health 2020;17.

Omar, N., Abdulazeez. A.M., Sengur, A., Al-Ali, S.G.S. (2020). Fused faster RCNNs for efficient detection of the license plates. In Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) Vol. 19, No. 2, August 2020, pp. 974~982.

Sowmya, V., Radha, R. (2021). Heavy-Vehicle Detection Based on YOLOv4 featuring Data Augmentation and Transfer-Learning Techniques. J. Phys.: Conf. Ser. 1911 012029.

Yohannes, E., Shih, T.K., Lin, C.Y., Enkhbat, A., Hong, C.Y., Utaminingrum, F.. (2021). Domain Adaptation Deep Attention Network for Automatic Logo Detection and Recognition in Google Street View. IEEE Acess, 10.1109/ACCESS.2021.3098713.

Y.L.X, Wang, Z. Maio, W.C, Tan. Data Augmentation for ML-driven Data Preparation and Integration. VLDB Endowment, Vol. 14, No. 12 ISSN 2150-8097. doi:10.14778/3476311.3476403

Roboflow, 2021. Roboflow Annotate. [online] Available at:

Tzutalin. LabelImg. Git code (2015). [online] Available at:

Jiang, Z., Zhao, L., Li, S., Jia, Y., 2020. Real-time object detection method based on improved YOLOv4-tiny.

D. Misra. (2020). Mish: A Self Regularized Non-Monotonic Activation Function. arXiv:1908.08681v3.

J., Nasir, A.A. Ramli, Michael. (2019). Design of Door Security System Based on Face Recognition with Arduino. JOIV Vol.3, No. 2 e-ISSN : 2549-9904, ISSN : 2549-9610.

H.N.M. Shah, M.Z.A. Rashid, Z. Kamis, M.S.N. Aras, N.M. Ali, F. Wasbari, M.N.F.B.A. Bakar. (2018). Design and Develop an Autonomous UAV Airship for Indoor Surveillance and Monitoring Applications. JOIV Vol.2, No. 1, e-ISSN : 2549-9904, ISSN : 2549-9610.

A.A. Fauzi, F. Utaminingrum, F. Ramdani. (2020). Road surface classification based on LBP and GLCM features using kNN classifier. Bulletin of Electrical Engineering and Informatics, Vol. 9, No. 4, pp. 1446-1453

F Utaminingrum, IK Somawirata, GD Naviri (2019). Alphabet Sign Language Recognition Using K-Nearest Neighbor Optimization,J. Comput., Vol. 14, No. 1, pp. 63-70