Algorithm for Determining the Distance Between Objects and their Linear Dimensions in the Field of View of an Active-Pulse Television Measuring System

An algorithm for determining the distance between observed objects and their linear dimensions in the field of view of an active-pulse television measuring system (AP TMS) using depth maps obtained by the multi-area range measurement method (MARMM) has been developed and experimentally studied. The possibility of using AP TMS and MARMM for the task of remotely measuring distances between observed objects and their linear dimensions has been assessed. The proposed algorithm allows determining the dimensions of objects and the distances between them based on known AP TMS parameters and information on the location of pixels on the matrix of the photodetector. The objects dimensions are understood as their linear quantities — width and height, determined in the metric coordinate system. Information on the location of pixels on the matrix includes the coordinates of pixels (row and column numbers of the matrix), which correspond to certain points of objects, in the coordinate system of the photodetector matrix (row and column numbers of the matrix). The algorithm adapts the known relationships of geometric optics for MARMM used in AP TMS. An experiment performed in laboratory conditions with an AP TMS prototype is used to test the algorithm. Results of the measurements show that the use of MARMM with 50 ns duration of the electron-optical converter strobing pulse provides the standart deviation (SD) of the calculated values relative to the reference (measured): for the objects sizes, SD = 0.01 m (no more than 5%); for distances between objects, SD = 0.12 m. Thus, the proposed algorithm can be used in practice for remote determination of linear dimensions of objects and distances between them using AP TMS.

1. Golitsyn A.A. Avtometriya, 2019, no. 5(55), pp. 107–114, DOI 10.15372/AUT20190515. (in Russ.)

2. Kuntsevich B.F., Kabashnikov V.P. Zurnal Prikladnoj Spektroskopii, 2020, no. 6(87), pp. 984–989. (in Russ.)

3. Baranov P., Tsytsulin A., Kurnikov A., & Chеrnogubov A. 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), 2019, October, pp. 0725–0728.

4. Surikov A.V., Leshenyuk N.S., Kuleshov V.K. Vestnik Universiteta grazhdanskoy zashchity MCHS Belarusi, 2021, no. 1(5), pp. 20–32, DOI 10.33408/2519-237X.2021.5-1.20. (in Russ.)

5. Gruzevich Yu.K., Alkov P.S., Balyasny L.M., Chistov O.V. Underwater Investigations and Robotics, 2024, no. 3(49), pp. 62–75, DOI 10.37102/1992-4429_2024_49_03_06. (in Russ.)

6. Ivanov V.I., Ivanov N.I. Zurnal Prikladnoj Spektroskopii, 2022, no. 6(89), pp. 858–868, DOI 10.47612/0514-7506-2022-89-6-858-868. (in Russ.)

7. Baranov P.S., Kurnikov A.S., Mantsvetov A.A., Pyatkov V.V. Extreme Robotics, 2019, no. 1(30), pp. 45–51.

8. Pengfei Wang, Hao Liu, Shaoping Qiu, Yu Liu, and Feng Huang, Appl. Opt. 2023, vol. 62, рр. 7633–7642.

9. Musikhin I.D., Kapustin V.V., Tislenko A.A., Movchan A., Zabuga S.A. Scientific Visualization, 2024, no. 1(16), pp. 38–51, DOI: 10.26583/sv.16.1.04.

10. Denisov A.V., Kapitonov D.A., Kurnikov A.S. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, no. 5(19), pp. 783–789, DOI: 10.17586/2226-1494-2019-19-5-783-789. (in Russ.)

11. Kapustin V.V., Movchan A.K. Voprosy radioelektroniki. Seriya: Tekhnika televideniya, 2023, no. 2, pp. 44–54. (in Russ.)

12. Kapustin V.V., Movchan A.K., Tislenko A.A. Avtometriya, 2024, no. 1(60), pp. 117–128, DOI 10.15372/AUT20240113. (in Russ.)

13. Patent RU 2811331 C1, G01S 17/02, Ustroystvo formirovaniya izobrazheniya karty dal’nostey do nablyudayemykh ob”yektov (Device for Forming Image of Map of Distances to Surveyed Objects), A. Movchan, M.I. Kuryachiy, V.V. Kapustin, N. Borodina, Patent application no. 2023124916, Priority 28.09.2023, Published 11.01.2024. (in Russ.)

14. Fowles G.R. Introduction to modern optics, Courier Corporation, 1989.

15. Heydarov P.Sh. Computer Optics, 2011, no. 2(35), pp. 275–280. (in Russ.)

16. Gonzalez R.C., Woods R.E. Digital Image Processing, NY, 2013, 1022 p.

17. Kapustin V.V., Zahlebin A.S., Movchan A.K. et al. Computer Optics, 2022, no. 6(46), pp. 948–954, DOI 10.18287/2412-6179-CO-1114.

18. Movchan A.K., Kapustin V.V., Kuryachiy M.I., Chaldina E.S. Proceedings of TUSUR University, 2020, no. 2(23), pp. 7–14, DOI 10.21293/1818-0442-2020-23-2-7-14. (in Russ.)