10.14489/td.2025.12.pp.030-039

2025, 12 December

DOI: 10.14489/td.2025.12.pp.030-039

Sudarev A. V., Kuvshinnikov V. S., Kovshov E. E.
INSTRUMENTAL METHODS AND MEANS OF AUTOMATED QUALITY EVALUATION OF RADIOGRAPHIC IMAGES IN DICONDE FORMAT
(pp. 30-39)

Abstract. The approach to evaluation and structuring of radiation nondestructive testing results in DICONDE digital format is considered. A quantitative mathematical model and a set of algorithms for evaluating the quality of radiographic images and information completeness of DICONDE-tags are given, and a comparative analysis of various tools for their processing is carried out. An ER-model of relational database structure for efficient storage and interaction with the results of radiation nondestructive testing has been developed. An example of analyzing the quality of the radiographic image of a welded joint, including interaction with the database and file storage of DICONDE-files, is considered to evaluate the performance of instrumental methods and software tools.

Keywords: radiation nondestructive testing, DICONDE, computer vision, mathematical modeling, algorithms and data structures.

+ - About the Authors Click to collapse

A. V. Sudarev (Federal State Budgetary Educational Institution of Higher Education “D. I. Mendeleev Russian University of Chemical Technology”, Moscow, Russia ) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.
V. S. Kuvshinnikov, E. E. Kovshov (Joint-Stock Company “Research and Development Institute of Construction Technology – Atomstroy”, Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.

+ - References Click to collapse

1. Muratov, D. A., & Vedernikova, I. I. (2022). Quality control of welded joints in the aviation industry. Nadezhnost i Dolgovechnost Mashin i Mekhanizmov, 185–188. [in Russian language]
2. Gerasimenko, A. A., Volkhin, R. V., Sukhonosov, Yu. E., & Marin, Ya. O. (2024). Analysis of the prospects for using radiographic crawlers in radiation non-destructive testing of aviation equipment. Simvol Nauki, (10-2-1), 64–66. [in Russian language]
3. Knyazev, S. V., Usoltsev, A. A., Kutsenko, A. I., et al. (2021). Modern methods and systems of non-destructive testing of cast and welded products for mining equipment. Naukoemkie Tekhnologii Razrabotki i Ispolzovaniya Mineralnykh Resursov, (7), 281–283. [in Russian language]
4. ASTM International. (2021). ASTM E2339-21: Standard practice for digital imaging and communication in nondestructive evaluation (DICONDE).
5. Huang, H. K. (2011). Short history of PACS. Part 1: USA. European Journal of Radiology, 78(2), 163–176. https://doi.org/10.1016/j.ejrad.2010.05.007
6. Kosach, A. A., & Kovshov, E. E. (2018). Automation of non-destructive testing data processing based on an artificial neural network. Cloud of Science, 5(3), 524–531. [in Russian language]
7. Kleizer, M. P., Kuvshinnikov, V. S., & Kovshov, E. E. (2024). Application of convolutional neural networks in processing raster images in non-destructive testing. Kontrol. Diagnostika, 27(6), 60–71. [in Russian language]. https://doi.org/10.14489/td.2024.06.pp.060-071
8. Malashin, I., Tynchenko, V., Nelyub, V., et al. (2024). Deep learning approach for pitting corrosion detection in gas pipelines. Sensors, 24(11), 3563. https://doi.org/10.3390/s24113563
9. Koshkin, O. V. (2021). Application of machine learning methods for solving problems of quality control of welded joints. In Information and computing technologies and their applications (pp. 87–92). Penza State Agrarian University. [in Russian language]
10. Leinenbach, F., Stumm, Ch., Krieg, F., & Schneider, A. (2024). Information reuse of nondestructive evaluation (NDE) data sets. Journal of Sensors and Sensor Systems, 13(1), 99–108. https://doi.org/10.5194/jsss-13-99-2024
11. Russian Institute for Standardization. (2024). GOST R ISO 19232-1-2024. Non-destructive testing. Image quality on radiographs. Part 1. Determination of image quality value using wire-type image quality indicators. [in Russian language].
12. Russian Institute for Standardization. (2024). GOST R ISO 19232-2-2024. Non-destructive testing. Image quality on radiographs. Part 2. Determination of image quality value using step/hole-type image quality indicators. [in Russian language].
13. Izdatelstvo Standartov. (1982). GOST 7512-82. Non-destructive testing. Welded joints. Radiographic method. [in Russian language].
14. International Organization for Standardization. (2011). ISO 16371-1: Non-destructive testing – Industrial computed radiography with storage phosphor imaging plates – Part 1: Classification of systems.
15. Standartinform. (2017). GOST ISO 17636-2-2017. Non-destructive testing of welded joints. Radiographic testing. Part 2. Techniques for X- and gamma-radiography using digital detectors. [in Russian language].
16. Villán, A. F. (2019). Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7. Packt Publishing Ltd.
17. Jähne, B. (2005). Digital image processing (6th ed.). Springer Science & Business Media.
18. OpenCvSharp. (n.d.). OpenCvSharp [Computer software]. GitHub. Retrieved June 10, 2025, from https://github.com/shimat/opencvsharp

+ - Purchase digital version of a single article Click to collapse

This article is available in electronic format (PDF).

The cost of a single article is 700 rubles. (including VAT 20%). After you place an order within a few days, you will receive following documents to your specified e-mail: account on payment and receipt to pay in the bank.

After depositing your payment on our bank account we send you file of the article by e-mail.

To order articles please copy the article doi:

10.14489/td.2025.12.pp.030-039

and fill out the form