Automatic segmentation by the method of interval fusion with preference aggregation when recognizing weld defects

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

Quality control of welding is usually carried out during the visual inspection process and is highly dependent on an operator experience. In the paper, it is proposed an approach to automatic detection and classification of a defective region, where segmentation of the analyzed photographic image of a weld (i.e., its division into defective and defect-free regions) is performed using the region growing procedure. The starting points for this procedure are selected by the authors' robust method of interval fusion with preference aggregation (IF&PA) on the base of image histogram analysis. Testing of the proposed approach for real life photographic images showed its ability to detect different types of weld defects with higher accuracy compared to traditional methods such as Otsu method and k-means.

Авторлар туралы

S. Muravyov

Tomsk Polytechnic University

Email: muravyov@tpu.ru
Tomsk, Russia

D. Nguyen

Tomsk Polytechnic University

Email: nguyen@tpu.ru
Tomsk, Russia

Әдебиет тізімі

  1. Nacereddine N., Goumeidane A.B., Ziou D. Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters // Computers in Industry. 2019. V. 108. P. 132-149.
  2. Муравьев С.В., Погадаева Е.Ю. Автоматизированное распознавание дефектов сварных соединений при визуальном контроле с использованием геометрических признаков // Дефектоскопия. 2020. №. 3. C. 49-57.
  3. Mery D., Pieringer C.Computer Vision for X-Ray Testing: Imaging, Systems, Image Databases, and Algorithms. Springer. 2021. 456 p. ISBN 978-3-030-56768-2.
  4. Zhang L., Zhang Y., Dai B., Chen B., Li Y. Welding defect detection based on local image enhancement // IET Image Processing. 2019. V. 13. P. 2647-2658.
  5. Yazid H., Arof H., Yazid H. Automated thresholding in radiographic image for welded joints // Nondestructive Testing and Evaluation. 2012. V. 27. No. 1. P. 69-80.
  6. Zahran O., Kasban H., El-Kordy M., Abd El-Samie F.E. Automatic weld defect identification from radiographic images // NDT & E International. 2013. V. 57. P. 26-35.
  7. ГОСТ Р ИСО 6520-1-2012. Сварка и родственные процессы. Классификация дефектов геометрии и сплошности в металлических материалах. Часть 1. Сварка плавлением.
  8. Mancas M., Gosselin B., Macq B. Segmentation using a region-growing thresholding // Proceedings of SPIE. Image Processing: Algorithms and Systems IV. 2005. V. 5672. P. 388-398.
  9. Sheela C.J.J., Suganthi G. Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm // Multimedia Tools and Applications. 2020. V. 79. P. 17483-17496.
  10. Muravyov S.V., Khudonogova L.I., Emelyanova E.Y.Interval data fusion with preference aggregation // Measurement. 2018. V. 116. P. 621-630.
  11. Muravyov S.V., Khudonogova L.I., Ho M.D. Analysis of heteroscedastic measurement data by the self-refining method of interval fusion with preference aggregation - IF&PA // Measurement. 2021. V. 183. P. 109851.
  12. Muravyov S.V. Ordinal measurement, preference aggregation and interlaboratory comparisons // Measurement. 2013. V. 46. No. 8. P. 2927-2935.
  13. Muravyov S.V., Emelyanova E.Y. Kemeny rule for preference aggregation: reducing all exact solutions to a single one // Measurement. 2021. V. 182. P. 109403.
  14. Гонсалес Р., Вудс Р. Цифровая обработка изображений. М.: Техносфера, 2012. 1104 с.
  15. Muravyov S.V., Pogadaeva E.Yu. Recognition Ability of Interval Fusion with Preference Aggregation in Weld Defects Images Analysis / 17th IMEKO TC10 Conference "Global Trends in Testing, Diagnostics & Inspection for 2030", Dubrovnik, Croatia. October 19-22. 2020. P. 271-276.
  16. Муравьев С.В., Маринушкина И.А. Обоснование выбора числа участников межлабораторных сличений // Научно-технические ведомости Санкт-Петербургского государственного политехнического университета. Информатика. Телекоммуникации. Управление. № 4. 2015. C. 81-90.
  17. Otsu N. A Threshold Selection Method from Gray-Level Histograms // IEEE Transactions on Systems, Man, and Cybernetics. 1979. V. 9. No. 1. P. 62-66.
  18. Zhan Y., Zhang G. An improved OTSU algorithm using histogram accumulation moment for ore segmentation // Symmetry. 2019. V. 11. No. 3. P. 431.
  19. Dhanachandra N., Manglem K., Chanu Y.J. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm // Procedia Computer Science. 2015. V. 54. P. 764-771.
  20. Levandowsky M., Winter D. Distance between Sets // Nature. 1971. V. 234. P. 34-35.
  21. Zhou D., Fang J., Song X., Guan C., Yin J., Dai Y., Yang R. IoU loss for 2D/3D object detection / Proceedings of the 7th International Conference on 3D Vision. Québec City, Canada, September 16-19, 2019. V. 1. P. 85-94.
  22. Dedkova A.A., Florinsky I.V. Geomorphometry and microelectronic metrology: Converged realms // Transactions in GIS. 2023. V. 27. No. 6. P. 1642-1661.

© Russian Academy of Sciences, 2023

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