Devising a method for measuring the motion parameters of industrial equipment in the quarry using adaptive parameters of a video sequence

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.248624

Keywords:

motion parameters, software-algorithmic processing of measuring video information, exponential smoothing, complexation

Abstract

The method and structural scheme of an information-measuring system for determining the parameters of objects' movements (technological equipment in the quarry for extracting block natural stone) have been proposed. A distinctive feature of time video sequences containing images of measured objects is their adaptation and adjustment in accordance with the intensity of movement and accuracy requirements for measurement results. Structural and software-algorithmic methods were also applied for improving the accuracy of measurements of motion parameters, namely: complexation of two measuring channels and exponential smoothing of digital references. One of the measuring channels is based on a digital video camera, the second is based on an accelerometer mounted on an object and two integrators. Exponential smoothing makes it possible to take into consideration the previous countdowns of movement parameters with weight coefficients. That ensures accounting for the existing patterns of movement of the object and reducing the errors when measuring the parameters of movement by (1.4...1.6) times.

The resulting solutions have been implemented in the form of an information and measurement system. The technological process of extracting blocks of natural stone in the quarry was experimentally investigated using a diamond-rope installation. Based on the contactless measurement of motion parameters, it is possible to ensure control over this process and improve the quality of blocks made of natural stone.

Based on the experimental study of measurement errors, recommendations were given for the selection of adaptive parameters of a video sequence, namely the size of images and the value of the inter-frame interval. In addition, methods for the software-algorithmic processing of measuring information were selected, specifically exponential smoothing and averaging the coordinates of the contour of an object, measured in 30 adjacent lines of the image

Author Biographies

Yurii Podchashynskyi, Zhytomyr Polytechnic State University

Doctor of Technical Sciences, Professor

Department of Metrology and Information and Measuring Tеechnique

Oksana Luhovykh, Zhytomyr Polytechnic State University

Department of Metrology and Information and Measuring Tеechnique

Vitaliy Tsyporenko, Zhytomyr Polytechnic State University

PhD

Department of Biomedical Engineering and Telecommunications

Valentyn Tsyporenko, Zhytomyr Polytechnic State University

PhD

Department of Biomedical Engineering and Telecommunications

References

  1. Levytskyi, V., Sobolevskyi, R., Korobiichuk, V. (2018). The optimization of technological mining parameters in a quarry for dimension stone blocks quality improvement based on photogrammetric techniques of measurement. Rudarsko Geolosko Naftni Zbornik, 33 (2), 83–89. doi: https://doi.org/10.17794/rgn.2018.2.8
  2. Korobiichuk, I., Shamray, V., Korobiichuk, V., Kryvoruchko, A., Iskov, S. (2021). Dose Measurement of Flocculants in Water Treatment of Stone Processing Plants. Automation 2021: Recent Achievements in Automation, Robotics and Measurement Techniques, 387–394. doi: https://doi.org/10.1007/978-3-030-74893-7_34
  3. Korobiichuk, I., Davydova, I., Korobiichuk, V., Shlapak, V., Panasiuk, A. (2021). Measurement of Qualitative Characteristics of Different Types of Wood Waste in the Forestries Zhytomyr Polissya. Automation 2021: Recent Achievements in Automation, Robotics and Measurement Techniques, 297–308. doi: https://doi.org/10.1007/978-3-030-74893-7_28
  4. Sobolevskyi, R., Korobiichuk, V., Levytskyi, V., Pidvysotskyi, V., Kamskykh, O., Kovalevych, L. (2020). Optimization of the process of efficiency management of the primary kaolin excavation on the curved face of the conditioned area. Rudarsko Geolosko Naftni Zbornik, 35 (1), 123–137. doi: https://doi.org/10.17794/rgn.2020.1.10
  5. Korobiichuk, I., Podchashinskiy, Y. (2021). Correlation mathematical model of video images with measuring information about geometrical parameters. 2021 25th International Conference on Methods and Models in Automation and Robotics (MMAR). doi: https://doi.org/10.1109/mmar49549.2021.9528487
  6. Korobiichuk, I., Podchashinskiy, Y., Luhovykh, O., Levkivskyi, V., Rzeplińska-Rykała, K. (2020). Theoretical Estimates of the Accuracy of Determination of Geometric Parameters of Objects on Digital Images. Automation 2020: Towards Industry of the Future, 289–299. doi: https://doi.org/10.1007/978-3-030-40971-5_27
  7. Korobiichuk, I., Podchashinskiy, Y., Lugovyh, O., Nowicki, M., Kachniarz, M. (2017). Algorithmic compensation of video image dynamic errors with measurement data about geometric and object motion parameters. Measurement, 105, 66–71. doi: https://doi.org/10.1016/j.measurement.2017.04.009
  8. Korobiichuk, I., Podchashinskiy, Y., Shapovalova, O., Shadura, V., Nowicki, M., Szewczyk, R. (2015). Precision increase in automated digital image measurement systems of geometric values. Advances in Intelligent Systems and Computing, 335–340. doi: https://doi.org/10.1007/978-3-319-23923-1_51
  9. Kvasnikov, V., Ornatskyi, D., Graf, M., Shelukha, O. (2021). Designing a computerized information processing system to build a movement trajectory of an unmanned aircraft. Eastern-European Journal of Enterprise Technologies, 1 (9 (109)), 33–42. doi: https://doi.org/10.15587/1729-4061.2021.225501
  10. Korobiichuk, V. V., Kotenko, V. V., Kalchuk, S. V., Sobolevskyi, R. V., Kisiel, O. O. (2011). Obladnannia dlia vydobuvannia blochnoho pryrodnoho kameniu. Zhytomyr: Vydavnytstvo Zhytomysrkoho derzhavnoho tekhnolohichnoho universytetu, 348
  11. Jambek, A. B., Juri, A. A. (2014). Low-energy motion estimation architecture using quadrant-based multi-octagon (QBMO) algorithm. Journal of Real-Time Image Processing, 12 (3), 623–632. doi: https://doi.org/10.1007/s11554-014-0426-x
  12. Sheikh, H. R., Sabir, M. F., Bovik, A. C. (2006). A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. IEEE Transactions on Image Processing, 15 (11), 3440–3451. doi: https://doi.org/10.1109/tip.2006.881959
  13. Korobiichuk, I., Lysenko, V., Opryshko, O., Komarchyk, D., Pasichnyk, N., Juś, A. (2018). Crop Monitoring for Nitrogen Nutrition Level by Digital Camera. Automation 2018, 595–603. doi: https://doi.org/10.1007/978-3-319-77179-3_56
  14. Rudyk, A. V. (2017). Analysis of the errors of MEMS accelerometers by the Allan variation method. The Journal of Zhytomyr State Technological University. Series: Engineering, 1 (79), 100–109. doi: https://doi.org/10.26642/tn-2017-1(79)-100-109
  15. Dudnik, A. (2018). Investigation of laser rangefinders with sensor network interface. Technology Audit and Production Reserves, 4 (2 (42)), 35–40. doi: https://doi.org/10.15587/2312-8372.2018.141190
  16. Cherepanska, I., Bezvesilna, O., Sazonov, A., Nechai, S., Pidtychenko, O. (2018). Development of artificial neural network for determining the components of errors when measuring angles using a goniometric software-hardware complex. Eastern-European Journal of Enterprise Technologies, 5 (9 (95)), 43–51. doi: https://doi.org/10.15587/1729-4061.2018.141290
  17. Kuz'min, S. Z. (1986). Osnovy proektirovaniya sistem tsifrovoy obrabotki radiolokatsionnoy informatsii. Moscow: Radio i svyaz', 432.
  18. Bahvalov, N. S., Zhidkov, N. P., Kobel'kov, G. M. (2008). Chislennye metody. Moscow: Binom, 636.
  19. Forsyth, D. A. Ponce, J. (2012). Computer Vision: A Modern Approach. Pearson Education, Inc, 761. Available at: https://eclass.teicrete.gr/modules/document/file.php/TM152/Books/Computer%20Vision%20-%20A%20Modern%20Approach%20-%20D.%20Forsyth,%20J.%20Ponce.pdf
  20. Lebedev, A. N. (1986). Veroyatnostnye metody v vychislitel'noy tekhnike. Moscow: Vysshaya shkola, 312.
  21. Samotokin, B. B. (2001). Lektsiyi z teoriyi avtomatychnoho keruvannia. Zhytomyr: Zhytomyrskyi inzhenerno-tekhnolohichnyi instytut, 508.
  22. Podchashynskyi, Yu. O., Luhovykh, O. O. (2020). Pat. No. 140691 UA. Prystriy dlia vymiriuvannia parametriv rukhu obiektiv. No. u201908229; declareted: 15.07.2019; published: 10.03.2020, Bul. No. 5. Available at: http://eztuir.ztu.edu.ua/jspui/bitstream/123456789/7687/1/140691.pdf

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Published

2021-12-29

How to Cite

Podchashynskyi, Y., Luhovykh, O., Tsyporenko, V., & Tsyporenko, V. (2021). Devising a method for measuring the motion parameters of industrial equipment in the quarry using adaptive parameters of a video sequence. Eastern-European Journal of Enterprise Technologies, 6(9 (114), 32–46. https://doi.org/10.15587/1729-4061.2021.248624

Issue

Section

Information and controlling system