Analyzing the effect of illumination on machine vision detection of the laser grid shape

DOI: https://doi.org/10.30686/1609-9192-2024-5S-110-115

Читать на русскоя языкеP.V. Cherkasov1, 2, Ya.V. Popinako1, 2, M.S. Nikitenko1, 2
1 Federal Research Center of Coal and Coal Chemistry of Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation
2 Kuzbass State Technological University named after T.F. Gorbachev, Kemerovo, Russian Federation

Russian Mining Industry №5 / 2024 p.110-115

Abstract: The main purpose of the study was to analyze the quality of laser grid shape detection in various ranges of illumination intensity for industrial application in conditions of underground and open-pit mining and on production sites. In particular, the dependences were established of the illumination level effect on the quality of the laser grid shape detection with the machine vision. Images were used as the study objects, while the subject was the machine vision algorithms and the values of the qualitative shape detection parameters. As a result, the authors have identified the conditions for the most effective shape detection of a laser grid projected onto the surface. In accordance with the developed methodology, the result of a series of laboratory tests was obtained with different light discreteness. In case of insufficient value of the parameter for a group of pixels, erosion and dilation operations were used, allowing to change the parameters of adjacent pixels to the values that would ensure the shape detection. The results are obtained in the form of qualitative dependence of the laser grid cells shape detection on illumination in laboratory conditions. The conclusion is made about the optimal illumination range, at which the quality of the laser grid shape detection is stable. The obtained results can be applied in various industries, including the mining industry, when measuring the objects volume with machine vision in combination with a laser grid.

Keywords: machine vision, image recognition, shape detection, light line projection, laser grid, controlled coal output, longwall top coal caving, volume measurement

Acknowledgments: The work was carried out within the framework of the state assignment of the Federal Research Center for Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences, project FWEZ-2024-0025 “Development of scientific foundations for the creation of autonomous and automated mining machines, equipment as well as technical and control systems based on promising digital and robotic technologies” (Reg. No. 1023033000581-6).

For citation: Cherkasov P.V., Popinako Ya.V., Nikitenko M.S. Analyzing the effect of illumination on machine vision detection of the laser grid shape. Russian Mining Industry. 2024;(5S):110–115. (In Russ.) https://doi.org/10.30686/1609-9192-2024-5S-110-115


Article info

Received: 19.08.2024

Revised: 02.10.2024

Accepted: 09.10.2024


Information about the author

Pavel V. Cherkasov – Engineer, Federal Research Center of Coal and Coal-Chemistry of Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Yaroslav V. Popinako – Engineer, Federal Research Center of Coal and Coal-Chemistry of Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation; https://orcid.org/0009-0007-2788-6074; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Mikhail S. Nikitenko – Cand. Sci. (Eng.), Head of Laboratory for Advanced Control Methods of Mining Engineering Systems, Federal Research Center of Coal and Coal-Chemistry of Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation; https://orcid.org/0000-0001-8752-1332; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


References

1. Ali D., Frimpong S. Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector. Artificial Intelligence Review. 2020;53:6025–6042. https://doi.org/10.1007/s10462-020-09841-6

2. Hyder Z., Siau K., Nah F. Artificial intelligence, machine learning, and autonomous technologies in mining industry. Journal of Database Management. 2019;30(2):67–79. http://doi.org/10.4018/JDM.2019040104

3. Barnewold L., Lottermoser B.G. Identification of digital technologies and digitalisation trends in the mining industry. International Journal of Mining Science and Technology. 2020;30(6):747–757. https://doi.org/10.1016/j.ijmst.2020.07.003

4. Калашников В.А., Соловьев В.И. Приложения компьютерного зрения в горнодобывающей промышленности. Прикладная информатика. 2023;18(1):4–21. https://doi.org/10.37791/2687-0649-2023-18-1-4-21 Kalashnikov V.A., Soloviev V.I. Applications of computer vision in the mining industry. Journal of Applied Informatics. 2023;18(1):4–21. (In Russ.) https://doi.org/10.37791/2687-0649-2023-18-1-4-21

5 Huang M.Q., Ninić J., Zhang Q.B. BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives. Tunnelling and Underground Space Technology. 2021;108:103677. https://doi.org/10.1016/j.tust.2020.103677

6. Nikitenko M.S., Khudonogov D.Yu., Popinako Ya.V., Kizilov S.A. Determining the route and roadway condition in front of autonomous vehicle. Proceedings of the Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024). Vol. 13217. 2024, 1321713. https://doi.org/10.1117/12.3036935

7. Кизилов С.А., Баловнев Е.А., Черкасов П.В., Никитенко М.С., Худоногов Д.Ю., Попинако Я.В. Подходы к автоматизированной оценке объема и состава горной массы в процессе выпуска угля на забойный конвейер. В кн.: Системы автоматизации (в образовании, науке и производстве) AS’2022: труды Всерос. науч.-практ. конф., г. Новокузнецк, 15–16 дек. 2022 г. Новокузнецк: СибГИУ; 2022. С. 20–25.

8. Никитенко М.С., Кизилов С.А., Захаров Ю.Н., Худоногов Д.Ю., Игнатова А.Ю. Измерение производительности питателя при выпуске угля из подкровельной толщи на основе технологии машинного зрения. Горные науки и технологии. 2022;7(4):264–273. https://doi.org/10.17073/2500-0632-2022-09-22 Nikitenko M.S., Kizilov S.A., Zakharov Yu.N., Khudonogov D.Yu., Ignatova A.Yu. Measurement of feeder performance during coal discharge from an underroof seam using machine vision. Mining Science and Technology (Russia). 2022;7(4):264–273. https://doi.org/10.17073/2500-0632-2022-09-22

9. Стародубов А.Н., Зиновьев В.В., Клишин В.И., Крамаренко В.А. Применение имитационного моделирования для исследования режимов выпуска угля подкровельной толщи. В кн.: Имитационное моделирование. Теория и практика: материалы 9-й Всерос. науч.-практ. конф. по имитационному моделированию и его применению в науке и промышленности, г. Екатеринбург, 16–18 окт. 2019 г. Екатеринбург: УрГПУ; 2019. С. 540–547.

10. Клишин В.И., Клишин С.В. Состояние и направление развития технологии разработки мощных угольных пластов механизированными крепями с выпуском. Известия Тульского государственного университета. Науки о Земле. 2019;(1):162–174. Klishin V.I., Klishin S.V. Current state and direction of development of thick coal seams exavation technology by powered roof supports with controlled coal discharge. Izvestiya Tul’skogo Gosudarstvennogo Universiteta. Nauki o Zemle. 2019;(1):162– 174. (In Russ.)

11. Клишин В.И., Анферов Б.А., Кузнецова Л.В., Клишин С.В., Худынцев Е.А. Секция механизированной крепи очистного забоя с устройством регулируемого выпуска угля. Патент РФ №2021131401, 04.04.2022.

12. Клишин В.И., Худынцев Е.А. Создание механизированных комплексов с выпуском для подземной разработки мощных угольных пластов. Вестник Кузбасского государственного технического университета. 2022;(6):96–106. https://doi.org/10.26730/1999-4125-2022-6-96-106 Klishin V.I., Khudyntsev Ye.A. Desining mechanized support complexes with coal release for underground development of thick coal seams. Bulletin of the Kuzbass State Technical University. 2022;(6):96–106. https://doi.org/10.26730/1999-4125-2022-6-96-106

13. Клишин В.И., Анферов Б.А., Кузнецова Л.В. Направления совершенствования разработки мощных пластов с выпуском угля подкровельной толщи. В кн.: Инновации в топливно-энергетическом комплексе и машиностроении (ТЭК-2017): материалы Междунар. науч.-практ. конф., г. Кемерово, 18–21 апр. 2021 г. Кемерово: КузГТУ им. Т.Ф. Горбачева; 2017. С. 57–63.

14. Клишин В.И., Шундулиди И.А., Ермаков А.Ю., Соловьев А.С. Технология разработки запасов мощных пологих пластов с выпуском угля. Новосибирск: Наука; 2013. 248 с.