mirtrМир транспортаWorld of Transport and Transportation1992-3252Russian University of Transport (RUT)10.30932/1992-3252-2019-17-62-72mirtr-1770Research ArticleНАУКА И ТЕХНИКАSCIENCE AND ENGINEERINGТехнология машинного зрения на локомотивах для идентификации путевых сигналовMachine Vision Technology for Locomotives to Identify Railway Colour-Light SignalsМинаковВ. А.MinakovV. I.

кандидат технических наук, доцент кафедры локомотивов

Омск

Ph.D. (Eng), Associate Professor of the Department of Locomotives

Omsk

vitalya_13@mail.ruФоменкоВ. К.FomenkoV. K.

кандидат технических наук, доцент кафедры локомотивов

Омск 

Ph.D. (Eng), Associate Professor of the Department of Locomotives

 Omsk

fomenkovk@mail.ruОмский государственный университет путей сообщения (ОмГУПС)РоссияOmsk State Transport University (OSTU)Russian Federation2019281220191766272Copyright © Минаков В.А., Фоменко В.К., 20192019Минаков В.А., Фоменко В.К.Minakov V.I., Fomenko V.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://mirtr.elpub.ru/jour/article/view/1770железная дорогалокомотивмашинное зрениесигнализациябезопасность движениятехническое зрениесвёрточные нейронные сетиrailwaylocomotivemachine visionsignallingtraffic safetymachine visionconvolutional neural networksReferencesПосмитюха А. А. Локомотивные приборы безопасности и контроль за их работой. – М.: Транспорт, 1992. – 61 с.Posmityukha, A. A. Locomotive safety devices and control over their operation [Lokomotivnie pribory bezopasnosti i control’ za ikh rabotoi]. Moscow, Transport publ., 1992, 61 p.Иванов Ю. А. Разработка локомотивной системы технического зрения: Автореф. дис... канд. юрид. наук. – М.: МГУПС, 2015. – 24 с.Ivanov, Yu. A. Development of a locomotive system of technical vision: Abstract of Ph.D. (Law) thesis [Razrabotka lokomotivnoi sistemy tekhnicheskogo zreniya: Avtoref. dis… kand. yur. nauk]. Moscow, MGUPS [RUT] publ., 2015, 24 p.Друки А. А. Применение сверточных нейронных сетей для выделения и распознавания автомобильных номерных знаков на изображениях со сложным фоном // Известия Томского политехнического университета. – 2014. – № 5. – С. 85–92.Druki, A. A. Application of convolutional neural networks for identification and recognition of car license plates in images with a complex background [Primenenie svertochnykh neironnykh setei dlya vydeleniya i raspoznavaniya avtomobilnykh nomernykh znakov na izobrazheniyakh so slozhnym fonom]. Izvestiya Tomskogo politekhnicheskogo universiteta, 2014, Iss. 5, pp. 85–92.Хайкин С. Нейронные сети: полный курс. 2-е изд. – М.: ООО «И. Д. Вильямс», 2006. – 1104 с. [Электронный ресурс]: https://studizba.com/files/show/djvu/1762–1-haykin-s–neyronnye-seti.html. Доступ 23.11.2019.Khaikin, S. Neural networks: full course [Neironnie seti: polniy kurs], 2nd ed. Moscow, «I. D. Williams» LLC, 2006, 1104 p. [Electronic resource]: https://studizba.com/files/show/djvu/1762–1-haykin-s–neyronnye-seti.html. Last accessed 23.11.2019.Chiang, Cheng-Chin; Ho, M.-C.; Liao, H.-S.; Pratama, Andi; Syu, W.-C. Detecting and recognizing traffic lights by genetic approximate ellipse detection and spatial texture layouts. International Journal of Innovative Computing, Information and Control, December 2011, Vol. 7, No. 12, pp. 6919–6934. [Электронный ресурс]: https://www.researchgate.net/publication/286958803_Detecting_and_recognizing_traffic_lights_by_genetic_approximate_ellipse_detection_and_spatial_texture_layouts. Доступ 23.11.2019.Chiang, Cheng-Chin;Ho, M.-C.; Liao, H.-S.; Pratama, Andi; Syu, W.-C. Detecting and recognizing traffic lights by genetic approximate ellipse detection and spatial texture layouts. International Journal of Innovative Computing, Information and Control, December 2011, Vol. 7, No. 12, pp. 6919–6934. [Electronic resource]: https://www.researchgate.net/publication/286958803_Detecting_and_recognizing_traffic_lights_by_genetic_approximate_ellipse_detection_and_spatial_texture_layouts. Last accessed 23.11.2019.Cortes, C., Vapnik, V. Support-vector networks. Machine Learning, 1995, Vol. 20, Iss. 3, pp. 273–297. DOI: https://doi.org/10.1023/A:1022627411411. [Электронный ресурс]: https://link.springer.com/content/pdf/10.1023/A:1022627411411.pdf. Доступ 23.11.2019.Cortes, C., Vapnik, V. Support-vector networks. Machine Learning, 1995, Vol. 20, Iss. 3, pp. 273–297. DOI: https://doi.org/10.1023/A:1022627411411. [Electronic resource]: https://link.springer.com/content/pdf/10.1023/A:1022627411411.pdf. Last accessed 23.11.2019.Diao, Yunfeng; Cheng, Wenming; Du, Run; Wang, Yaqing; Zhang, Jun. Vision-based detection of container lock holes using a modified local sliding window method. EURASIP Journal on Image and Video Processing, 2019, Vol. 69. [Электронный ресурс]: https://link.springer.com/article/10.1186%2Fs13640-019-0472-1. Доступ 29.12.2019.Diao, Yunfeng; Cheng, Wenming; Du, Run; Wang, Yaqing; Zhang, Jun. Vision-based detection of container lock holes using a modified local sliding window method. EURASIP Journal on Image and Video Processing, 2019, Vol. 69. [Electronic resource]: https://link.springer.com/article/10.1186%2Fs13640-019-0472-1. Last accessed 29.12.2019.Джули А., Пал С. Библиотека Keras – инструмент глубокого обучения. – М.: ДМК Пресс, 2018. – 294 с.Julie, A., Pal, S. Keras Library – A Deep Learning Tool [Biblioteka Keras – instrument glubokogo obucheniya]. Moscow, DMK Press, 2018, 294 p.Kecman, V., Melki, G. Fast Online Algorithms for Support Vector Machines Models and Experimental Studies. IEEE South East Conference (SoutheastCon 2016), Virginia, USA, 2016. pp. 26–31. [Электронный ресурс]: https://www.researchgate.net/publication/303257413_Fast_Online_Algorithms_for_Support_Vector_Machines_Models_and_Experimental_Studies. Доступ 29.12.2019.Kecman, V., Melki, G. Fast Online Algorithms for Support Vector Machines Models and Experimental Studies. IEEE South East Conference (SoutheastCon 2016), Virginia, USA, 2016. pp. 26–31. [ElectronicPlatt, J. C. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Microsoft Research, Technical Report MSR-TR-98-14, 1998. [Электронный ресурс]: https://www.researchgate.net/publication/2624239_Sequential_Minimal_Optimization_A_Fast_Algorithm_for_Training_Support_Vector_Machines. Доступ 29.12.2019.resource]: https://www.researchgate.net/publication/303257413_Fast_Online_Algorithms_for_Support_Vector_Machines_Models_and_Experimental_Studies. Last accessed 29.12.2019.Platt, J. C. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Microsoft Research, Technical Report MSR-TR-98-14, 1998. [Electronic resource]: https://www.researchgate.net/publication/2624239_Sequential_Minimal_Optimization_A_Fast_Algorithm_for_Training_Support_Vector_Machines. Last accessed 29.12.2019.Platt, J. C. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, Microsoft Research, Technical Report MSR-TR-98-14, 1998. [Electronic resource]: https://www.researchgate.net/publication/2624239_Sequential_Minimal_Optimization_A_Fast_Algorithm_for_Training_Support_Vector_Machines. Last accessed 29.12.2019.

The authors declare that there are no conflicts of interest present.