A method to optimize parameters of software and hardware for dump truck remote monitoring systems using QR code recognition

R.N. Safiullin¹, Haotian Tian¹, R.R. Safiullin¹, M.R. Bashirov², E.G. Kozin^3
1 Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation
² Interregional Territorial Department of the Federal Service for Transport Supervision in the North-West Federal District, St. Petersburg, Russian Federation
³ Saint Petersburg State Unitary Enterprise "Petersburg Metro", Saint Petersburg, Russian Federation
Russian Mining Industry №1S / 2025 p. 14-20

Abstract: This paper proposes a monitoring system based on the QR code technology, which includes a control module, QR code generation module, recognition module, data transmission and processing module, and a central monitoring platform. An optimization experimental study based on the response surface method was conducted in order to improve the QR code identification reliability while the vehicle is travelling, and investigate the relationship between several influencing factors and the QR code identification reliability. The results show that the influence rate of various factors on reliability of QR code recognition is characterized with the following pattern: travelling speed > reading distance > QR code size > encoded characters. Under the optimal experimental conditions, i.e. the reading distance of 141.45 mm, the QR code size of 34.58 mm, 100 bytes of encoded characters and travelling speed of 2,98 m/min, the average QR code readability value was 95%. The results show hat optimizing parameters of the influencing factors can effectively improve the QR code recognition rate, thereby improving the functional efficiency of the monitoring system.

Keywords: artificial intelligence, automated monitoring systems, hardware and software means of control, identification, recognition object, QR code

For citation: Safiullin R.N., Tian H., Safiullin R.R., Bashirov M.R., Kozin E.G. A method to optimize parameters of software and hardware for dump truck remote monitoring systems using QR code recognition. Russian Mining Industry. 2025;(1S):14–20. https://doi.org/10.30686/1609-9192-2025-1S-14-20

Article info

Received: 29.12.2024

Revised: 31.01.2025

Accepted: 03.02.2025

Information about the authors

Ravill N. Safiullin – Dr. Sci. (Eng.), Professor, Professor of the Department of Transport and Technological Processes and Machines, Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation; https://orcid.org/0000-0002-8765-6461; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Haotian Tian – Postgraduate Student of the Department of Transport and Technological Processes and Machines, Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation; https://orcid.org/0000-0002-8963-109X; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ruslan R. Safiullin – Cand. Sci. (Eng.), Associate Professor, Associate Professor of the Department of Transport and Technological Processes and Machines, Empress Catherine II Saint Petersburg Mining University, Saint Petersburg, Russian Federation; https://orcid.org/0000-0003-2315-3678; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Mansur R. Bashirov – Deputy Head of the Department (Gosavtodornadzor - Saint Petersburg), Interregional Territorial Department of the Federal Service for Transport Supervision in the North-West Federal District, Saint Petersburg, Russian Federation

Evgeny G. Kozin – Cand. Sci. (Eng.), Head of the St. Petersburg State Unitary Enterprise “St. Petersburg Metro”, St. Petersburg, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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