Devising an approach to conducting full-scale experiments in physics that provides for the improved efficiency when measuring physical quantities

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.333084

Keywords:

video processing, laboratory setups, YOLOv8n, Hough method, computer vision, online learning

Abstract

The object of this study is a procedure for measuring physical quantities under laboratory conditions at educational institutions. The issue related to this case is the lack of any comprehensive method and technical solution suitable for the experimental study of physics in both offline and online learning formats. To solve this problem, an approach has been proposed, based on computer vision technology and training special neural models to recognize objects in video frames that perform mechanical movement.

The idea of the proposed approach is based on the hypothesis that by measuring the position of an object in video frames with sufficient accuracy, it is possible to determine the functional type of the law of its motion. Further, knowing the function of the law of motion, it is possible to calculate any physical quantities describing the process under consideration. The idea is implemented in the form of a technical solution, which is a set of prototypes of automated laboratory devices.

The choice of the method for determining the law of motion was carried out using the analysis of the recognition error, measurement error, speed and resistance to external conditions of the Hough algorithmic method and the YOLOv8n neural network model. It is shown that the neural network method YOLOv8n has very high accuracy but low performance. The Hough method shows high performance but lower accuracy and resistance to external conditions. It was found that the accuracy of the YOLOv8n method is 4 times higher, but the execution speed is 10 times lower than that of the Hough method. However, in the case of artificial lighting and fixing the distance from the camera to objects, the Hough method provides 99.9% accuracy in recognizing an object in video frames.

The obtained prototypes of devices can be used for further research to determine their impact on the quality of physics education

Author Biographies

Bekbolat Medetov, L.N. Gumilyov Eurasian National University

PhD, Associate Professor

Department of Radio Engineering, Electronics and Telecommunications

Ainur Zhetpisbayeva, L.N. Gumilyov Eurasian National University

PhD, Associate Professor

Department of Radio Engineering, Electronics and Telecommunications

Tansaule Serikov, S.Seifullin Kazakh Agrotechnical Research University

PhD, Associate Professor

Department of Radio Engineering, Electronics and Telecommunications

Botagoz Khamzina, S.Seifullin Kazakh Agrotechnical Research University

Doctor of Pedagogical Sciences, Associate Professor

Department of Physics and Chemistry

Ainur Akhmediyarova, Satbayev University

PhD

Department of Software Engineering

Asset Yskak, Ghalam LLP

Master of Engineering Science

Software Development Department

Dauren Zhexebay, Al-Farabi Kazakh National University

PhD, Senior Lecturer

Department of Electronics and Astrophysics

Nurtay Albanbay, Satbayev University

PhD

Department of Cybersecurity, Information Processing and Storage

References

  1. Illeperuma, G. D., Sonnadara, D. U. J. (2017). Computer vision based object tracking as a teaching aid for high school physics experiments. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 1–6. https://doi.org/10.1109/eecsi.2017.8239112
  2. Singh, L., Gupta, A., Nigam, A. (2022). Vibration analysis of simple pendulum using computer vision method. Journal of Mechanics and Design, 06 (1), 11–18.
  3. Guedri, B., Guedri, N., Gharbi, R. (2023). A New Approach for Real-Time Camera-Object Distance Measurement Through Computer Vision. 2023 IEEE Third International Conference on Signal, Control and Communication (SCC), 1–5. https://doi.org/10.1109/scc59637.2023.10527537
  4. Pattanashetty, K. C., Kumar, R., Pandian, S. R. (2016). Web-based physics experiments in dynamics using image processing. 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 1–5. https://doi.org/10.1109/icpeices.2016.7853575
  5. Nakagawa, G., Fujii, K. (2017). A Learning Material for Physics Experiment with High-Accuracy Using Computer Vision Technique. 2017 5th Intl Conf on Applied Computing and Information Technology/4th Intl Conf on Computational Science/Intelligence and Applied Informatics/2nd Intl Conf on Big Data, Cloud Computing, Data Science (ACIT-CSII-BCD), 86–89. https://doi.org/10.1109/acit-csii-bcd.2017.71
  6. Martín-Ramos, P., Gomes, M. S. M. N. F., Silva, M. R. (2018). Newton’s cradle. Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality, 71–77. https://doi.org/10.1145/3284179.3284195
  7. Bogdan, G., Ovidiu, T., Cristina, M., Ștefan, A. (2024). Digital electricity experiments with Raspberry Pi. Romanian Reports in Physics, 76 (4), 905–905. https://doi.org/10.59277/romrepphys.2024.76.905
  8. Çoban, A., Akat, E., Erdoğan, A. C. (2022). Two different experiments with the rope-attached sphere by using Arduino. Physics Education, 58 (1), 015022. https://doi.org/10.1088/1361-6552/aca19d
  9. Bach, R. A., Trantham, K. W. (2007). Automated two-dimensional position measurements with computer vision. American Journal of Physics, 75 (1), 48–52. https://doi.org/10.1119/1.2348892
  10. Khatri, P., Chhatre, U., Kadge, S. (2021). Visual Vibration Analysis of Vibrating Object at Low Frequency. 2021 6th International Conference for Convergence in Technology (I2CT), 1–5. https://doi.org/10.1109/i2ct51068.2021.9418210
  11. Cheng, L., Niu, W.-C., Zhao, X.-G., Xu, C.-L., Hou, Z.-Y. (2021). Design and implementation of college physics teaching platform based on virtual experiment scene. International Journal of Electrical Engineering & Education, 62 (1), 73–86. https://doi.org/10.1177/0020720920984688
  12. Hough Line Transform. Available at: https://docs.opencv.org/4.x/d6/d10/tutorial_py_houghlines.html
  13. Python Usage. Available at: https://docs.ultralytics.com/usage/python/
Devising an approach to conducting full-scale experiments in physics that provides for the improved efficiency when measuring physical quantities

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Published

2025-06-25

How to Cite

Medetov, B., Zhetpisbayeva, A., Serikov, T., Khamzina, B., Akhmediyarova, A., Yskak, A., Zhexebay, D., & Albanbay, N. (2025). Devising an approach to conducting full-scale experiments in physics that provides for the improved efficiency when measuring physical quantities. Eastern-European Journal of Enterprise Technologies, 3(5 (135), 59–69. https://doi.org/10.15587/1729-4061.2025.333084

Issue

Section

Applied physics