Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current Defectograms
To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including eddy-current flaw detection methods. An automatic analysis of large data sets (defectograms) that come from the corresponding equipment is an actua...
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| Format: | Article |
| Language: | English |
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Yaroslavl State University
2020-09-01
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| Series: | Моделирование и анализ информационных систем |
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| Online Access: | https://www.mais-journal.ru/jour/article/view/1351 |
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| _version_ | 1850025689162448896 |
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| author | Egor V. Kuzmin Oleg E. Gorbunov Petr O. Plotnikov Vadim A. Tyukin Vladimir A. Bashkin |
| author_facet | Egor V. Kuzmin Oleg E. Gorbunov Petr O. Plotnikov Vadim A. Tyukin Vladimir A. Bashkin |
| author_sort | Egor V. Kuzmin |
| collection | DOAJ |
| description | To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including eddy-current flaw detection methods. An automatic analysis of large data sets (defectograms) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks in defectograms. This article is devoted to the problem of recognizing images of long structural elements of rails in eddy-current defectograms. Two classes of rail track structural elements are considered: 1) rolling stock axle counters, 2) rail crossings. Long marks that cannot be assigned to these two classes are conditionally considered as defects and are placed in a separate third class. For image recognition of structural elements in defectograms a convolutional neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 30 x 140 points. |
| format | Article |
| id | doaj-art-e8ff5fe6e5a5431fa77b74b2f0a71422 |
| institution | DOAJ |
| issn | 1818-1015 2313-5417 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | Yaroslavl State University |
| record_format | Article |
| series | Моделирование и анализ информационных систем |
| spelling | doaj-art-e8ff5fe6e5a5431fa77b74b2f0a714222025-08-20T03:00:45ZengYaroslavl State UniversityМоделирование и анализ информационных систем1818-10152313-54172020-09-0127331632910.18255/1818-1015-2020-3-316-3291010Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current DefectogramsEgor V. Kuzmin0Oleg E. Gorbunov1Petr O. Plotnikov2Vadim A. Tyukin3Vladimir A. Bashkin4P.G. Demidov Yaroslavl State UniversityCenter of Innovative Programming, NDDLabCenter of Innovative Programming, NDDLabCenter of Innovative Programming, NDDLabP.G. Demidov Yaroslavl State UniversityTo ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including eddy-current flaw detection methods. An automatic analysis of large data sets (defectograms) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks in defectograms. This article is devoted to the problem of recognizing images of long structural elements of rails in eddy-current defectograms. Two classes of rail track structural elements are considered: 1) rolling stock axle counters, 2) rail crossings. Long marks that cannot be assigned to these two classes are conditionally considered as defects and are placed in a separate third class. For image recognition of structural elements in defectograms a convolutional neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 30 x 140 points.https://www.mais-journal.ru/jour/article/view/1351nondestructive testingeddy-current testingrail flaw detectionautomated analysis of defectogramsneural networks |
| spellingShingle | Egor V. Kuzmin Oleg E. Gorbunov Petr O. Plotnikov Vadim A. Tyukin Vladimir A. Bashkin Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current Defectograms Моделирование и анализ информационных систем nondestructive testing eddy-current testing rail flaw detection automated analysis of defectograms neural networks |
| title | Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current Defectograms |
| title_full | Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current Defectograms |
| title_fullStr | Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current Defectograms |
| title_full_unstemmed | Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current Defectograms |
| title_short | Application of Convolutional Neural Networks for Recognizing Long Structural Elements of Rails in Eddy-Current Defectograms |
| title_sort | application of convolutional neural networks for recognizing long structural elements of rails in eddy current defectograms |
| topic | nondestructive testing eddy-current testing rail flaw detection automated analysis of defectograms neural networks |
| url | https://www.mais-journal.ru/jour/article/view/1351 |
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