Analysis of the Possibilities of Reading Instrument Readings Using Machine Vision Algorithms

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

This paper examines methods and devices designed for reading and remote transmission of pointer instrument readings. The range of tasks solved using machine vision tools is considered, and their applicability to the task at hand is assessed. The use of a machine vision algorithm integrated into a mobile application for reading pointer instrument readings is proposed.

Sobre autores

Mikhail Shlyakhov

Bauman Moscow State Technical University

Autor responsável pela correspondência
Email: tog23@mail.ru

master

Rússia, Moscow

Elizaveta Petrenko

Bauman Moscow State Technical University

Email: arbuzov41@mail.ru

Associate Professor of the Department of RK-9 "Automation of Technological Processes and Production", Candidate of Technical Sciences

Rússia, Moscow

Bibliografia

  1. M. I. Beliy, E. V. Antonets, G.A. Emelyanov. Device for automatic reading of pointer instrument readings. Patent USSR No. 669193 (In Russ).
  2. S. K. Kiselyov, V. A. Mishin. Method for automatic reading of readings from scales of pointer measuring instruments during their verification. Patent RF No. 2035746 (In Russ).
  3. Nadvotskaya V. V. Development of a device for reading household metering devices. Polzunovskiy almanakh – 2021. – № 4. – p. 113-115. – EDN HYCLVH.
  4. S. K. Kiselyov, D. G. Shabayev. Usage of a test method to improve the accuracy of measurements in automated optical reading of panel pointer instruments. Vestnik Ulyanovskogo politeckhnicheskogo instiuta. – 2005. – № 1(29). – С. 44-46. – EDN REJVRL.
  5. Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives / C. Liu, S. Li, F. Chang, Y. Wang // IEEE Access. – 2019. – Vol. 7. – P. 86578-86596. – doi: 10.1109/ACCESS.2019.2924947. – EDN RGDZSS.
  6. S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel, ‘‘Detection of traffic signs in real-world images: The German traffic sign detection benchmark,’’ in Proc. Int. Joint Conf. Neural Netw., Dallas, TX, USA, Aug. 2013, pp. 1–8.
  7. J. Greenhalgh and M. Mirmehdi, ‘‘Real-time detection and recognition of road traffic signs,’’ IEEE Trans. Intell. Transp. Syst., vol. 13, no. 4,pp. 1498–1506, Dec. 2012.
  8. H. S. Lee and K. Kim, ‘‘Simultaneous traffic sign detection and boundary estimation using convolutional neural network,’’ IEEE Trans. Intell. Transp. Syst., vol. 19, no. 5, pp. 1652–1663, May 2018.
  9. . Manukyan R. L. (eds). Machine vision. Basic components of machine vision. In: R. L. Manukyan, I. V. Petrashin // MODERN SCIENCE: TOPICAL ISSUES, ACHIEVEMENTS AND INNOVATIONS: Proceedings of the VII International scientific and practical conference, part 2. Penza: «Nauka I prosveschenie»: Gulyaev G. Y: 2019: p. 295-297. – EDN HEJCRG
  10. P. Yakimov and V. Fursov, ‘‘Traffic signs detection and tracking usingmodified Hough transform,’’ in Proc. Conf. E-Bus. Telecommun., Colmar,France, Jul. 2015, pp. 1–7.
  11. J. Greenhalgh and M. Mirmehdi, ‘‘Recognizing text-based traffic signs,’’ IEEE Trans. Intell. Transp. Syst., vol. 16, no. 3, pp. 1360–1369, Jun. 2015.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar


Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).