Russian Journal of Nondestructive Testing

, Volume 55, Issue 9, pp 672–686 | Cite as

Identification of Materials in Fragments of Large-Sized Objects in Containers by the Dual-Energy Method

  • S. P. OsipovEmail author
  • E. Yu. Usachev
  • S. V. ChakhlovEmail author
  • S. A. Shchetinkin
  • S. Song
  • G. Zhang
  • A. V. Batranin
  • O. S. Osipov


The mathematical and simulation models of image formation in a system for the recognition of materials of internal fragments of containers by the dual-energy method are developed. A series of computational experiments were carried out on the recognition of materials inside shipping containers without and with compensation for the influence of the factor being analyzed on the quality of identification. The thickness of the prefilter, the ADC digit capacity, the ratio of the numbers of low- and high-energy bremsstrahlung pulses, and the dimensions of the averaging filter window were varied. An algorithm has been developed to compensate for the influence of the container wall thickness on the identification quality. The algorithm is based on the statistical processing of radiographic images of the object. Its effectiveness has been experimentally proved.


bremsstrahlung dual-energy method material identification mass thickness effective atomic number 



The study was conducted at Tomsk Polytechnic University as part of a grant from the Competitiveness Enhancement Program of Tomsk Polytechnic University and with financial support from PowerScan Ltd Company (China).


  1. 1.
    Ogorodnikov, S. and Petrunin, V., Processing of interlaced images in 4–10 MeV dual energy customs system for material recognition, Phys. Rev. Spec. Top. Accel. Beams, 2002, vol. 5, no. 10, article no. 104701.CrossRefGoogle Scholar
  2. 2.
    Wang, X.W., Li, J.M., Kang, K.J, Tang, C.X., Zhang, L., Chen, Z., Li, Y.J., Z.H., Material discrimination by high-energy X-ray dual-energy imaging, High Energy Phys. Nucl. Phys., 2007, vol. 31, no. 11, pp. 1076–1081.Google Scholar
  3. 3.
    Osipov, S.P., Chakhlov, S.V., Osipov, O.S., Shtein, A.M., and Strugovtsev, D.V., About accuracy of the discrimination parameter estimation for the dual high-energy method, IOP Conf. Ser: Mater. Sci. Eng., IOP Publ., 2015, vol. 81, no. 1, article no. 012082.CrossRefGoogle Scholar
  4. 4.
    Oh, K., Kim, J., Kim, S., Chae, M., Lee, D., Cha, H., and Lee, B., Evaluation and optimization of an image acquisition system for dual-energy cargo inspections, IEEE Trans. Nucl. Sci., 2018, vol. 65, no. 9, pp. 2657–2661.CrossRefGoogle Scholar
  5. 5.
    Shikhaliev, P.M., Megavoltage cargo radiography with dual energy material decomposition, Nucl. Instrum. Methods Phys. Res.,Sect. A, 2018, vol. 882, pp. 158–168.Google Scholar
  6. 6.
    Chen, G., Bennett, G., and Perticone, D., X-ray radiography for automatic high-Z material detection, Nucl. Instrum. Methods Phys. Res., Sect. B, 2007, vol. 261, nos. 1–2, pp. 356–359.Google Scholar
  7. 7.
    Fu, K., Ranta, D., Guest, C., and Das, P., The application of wavelet denoising in material discrimination system, Image Process.: Machine Vision Appl. III, Int. Soc. Opt. Photonics, 2010, vol. 7538, article no. 75380Z.Google Scholar
  8. 8.
    Li, L., Zhao, T., and Chen, Z., First dual MeV energy X-ray CT for container inspection: design, algorithm, and preliminary experimental results, IEEE Access., 2018, vol. 6, pp. 45534–45542.CrossRefGoogle Scholar
  9. 9.
    Liu, Y., Sowerby, B.D., and Tickner, J.R., Comparison of neutron and high-energy X-ray dual-beam radiography for air cargo inspection, Appl. Radiat. Isot., 2008, vol. 66, no. 4, pp. 463—473.CrossRefGoogle Scholar
  10. 10.
    Novikov, V.L., Ogorodnikov, S.A., and Petrunin, V.I., Dual energy method of material recognition in high energy introscopy systems, Vopr. At. Nauki Tekh., 1999, vol. 4, no. 2, pp. 93–95.Google Scholar
  11. 11.
    Zhang, G., Zhang, L., and Chen, Z., An HL curve method for material discrimination of dual energy X-ray inspection systems, IEEE Nucl. Sci. Symp. Conf. Rec.2005, IEEE, 2005, vol. 1, pp. 326–328.Google Scholar
  12. 12.
    Chen, Z.Q., Zhao, T., and Li, L., A curve-based material recognition method in MeV dual-energy X-ray imaging system, Nucl. Sci. Tech., 2016, vol. 27, no. 1, pp. 1–8.CrossRefGoogle Scholar
  13. 13.
    Osipov, S.P., Temnik, A.K., and Chakhlov, S.V., The effects of physical factors on the quality of the dual high-energy identification of the material of an inspected object, Russ. J. Nondestr. Test., 2014, vol. 50, no. 8, pp. 491–498.CrossRefGoogle Scholar
  14. 14.
    Rogers, T.W., Jaccard, N., Morton, E.J., and Griffin, L.D., Automated X-ray image analysis for cargo security: critical review and future promise, J. X-Ray Sci. Technol., 2017, vol. 25, no. 1, pp. 33–56.CrossRefGoogle Scholar
  15. 15.
    Kovalenko, N.O., Naydenov, S.V., Pritula, I.M., and Galkin, S.N., II sulfides and II selenides: growth, properties, and modern applications, in Single Crystals of Electronic Materials, Woodhead Publ., 2019, pp. 303–330.Google Scholar
  16. 16.
    Osipov, S.P., Chakhlov, S.V., Osipov, O.S., Li, S., Sun, X., Zheng, J., Hu, X., and Zhang, G., Physical and technical restrictions of materials recognition by the dual high energy X-ray imaging, Int. J. Appl. Eng. Res., 2017, vol. 12, no. 23, pp. 13127–13136.Google Scholar
  17. 17.
    Andrews, J.T.A., Jaccard, N., Rogers, T.W., and Griffin, L.D., Representation-learning for anomaly detection in complex x-ray cargo imagery, Anomaly Detect. Imaging X-Rays (ADIX) II, Int. Soc. Opt. Photonics, 2017, vol. 10187, article no. 101870E.Google Scholar
  18. 18.
    Pashby, J., Glenn, S., Divin, C., and Martz, H., Radiation Detection and Dual-Energy X-Ray Imaging for Port Security, Livermore, CA: Lawrence Livermore Natl. Lab. (LLNL), 2017, no. LLNLTR-736549.Google Scholar
  19. 19.
    Kolokytha, S., Flisch, A., Lüthi, T., Plamondon, M., Visser, W., Schwaninger, A., Hardmeier, D., Costin, M., Vienne, C., Sukowski, F., Hassler, U., Dorion, I., Gadi, N., Maitrejean, S., Marciano, A., Canonica, A., Rochat, E., Koomen, G., and Slegt, M., Creating a reference database of cargo inspection X-ray images using high energy radiographs of cargo mock-ups, Multimedia Tools Appl., 2018, vol. 77, no. 8, pp. 9379–9391.CrossRefGoogle Scholar
  20. 20.
    Storm, L. and Israel, H.I., Photon cross sections from 1 keV to 100 MeV for elements \(Z = 1\) to \(Z = 100\), At. Data Nucl. Data Tables, 1970, vol. 7, no. 6, pp. 565–681.CrossRefGoogle Scholar
  21. 21.
    Berger, M.J. and Hubbell, J.H., XCOM: Photon Cross Sections on a Personal Computer, Washington, DC: Natl. Bur. Stand., Center Radiat. Res., 1987, no. NBSIR-87-3597.Google Scholar
  22. 22.
    X-ray mass attenuation coefficients. NIST standard reference database 126. URL: Scholar
  23. 23.
    Udod, V.A., Osipov, S.P., and Wang, Y., The mathematical model of image, generated by scanning digital radiography system, IOP Conf. Ser.: Mater. Sci. Eng., IOP Publ., 2017, vol. 168, no. 1, article no. 012042.CrossRefGoogle Scholar
  24. 24.
    Aliev, F.K., Alimov, G.R., Muminov, A.T., Osmanov, B.S., and Skvortsov, V.V., Simulation of experiment on total external reflection of electron bremsstrahlung, Tech. Phys., 2005, vol. 50, no. 8, pp. 1053–1057.CrossRefGoogle Scholar
  25. 25.
    Ali, E.S.M. and Rogers, D.W.O., Functional forms for photon spectra of clinical linacs, Phys. Med. Biol., 2011, vol. 57, pp. 31—50.CrossRefGoogle Scholar
  26. 26.
    Scharf, W. and Wieszczycka, W., Electron accelerators for industrial processing—a review, AIP Conf. Proc., AIP, 1999, vol. 475, no. 1, pp. 949–952.Google Scholar
  27. 27.
    Stein, M., Kasyanov, V.A., Chakhlov, V.L., Macleod, J., Marjoribanks, P., and Hubbard, S., Small-size betatrons for radiographic inspection, 16th World Conf. NDT, 2004. URL: wcndt2004/pdf/radiography/104_stein.pdfGoogle Scholar
  28. 28.
    Kutsaev, S., Agustsson, R., Arodzero, A., Boucher, S., Hartzell, J., Murokh A., O’Shea, and Smirnov, A.Y., Electron accelerators for novel cargo inspection methods, Phys. Procedia, 2017, vol. 90, pp. 115–125.CrossRefGoogle Scholar
  29. 29.
    Mizusako, F., Ogasawara, K., Kondo, K., Saito, F., and Tamura, H., Flash x-ray radiography using imaging plates for the observation of hypervelocity objects, Rev. Sci. Instrum., 2005, vol. 76, no. 2, article no. 025102.CrossRefGoogle Scholar
  30. 30.
    Bae, U., Shamdasani, V., Managuli, R., and Kim, Y., Fast adaptive unsharp masking with programmable mediaprocessors, J. Digital Imaging, 2003, vol. 16, no. 2, pp. 230–239.CrossRefGoogle Scholar
  31. 31.
    Sarangapani, R., Jose, M.T., Srinivasan, T.K., and Venkatraman, B., Determination of efficiency of an aged HPGe detector for gaseous sources by self absorption correction and point source methods, J. Instrum., 2017, vol. 12, no. 7, article no. T07006.CrossRefGoogle Scholar
  32. 32.
    Gavrila, C., Petrehus, V., and Gruia, I., Using Radon transform in image reconstruction, Math. Model. Civ. Eng., 2010, no. 3. URL: Scholar
  33. 33.
    Chakhlov, S.V., Kasyanov, S.V., Kasyanov, V.A., Osipov, S.P., Stein, M.M., Stein, A.M., and Xiaoming, S., Betatron application in mobile and relocatable inspection systems for freight transport control, J. Phys. Conf. Ser., IOP Publ., 2016, vol. 671, no. 1, article no. 012024.Google Scholar
  34. 34.
    Scientific educational cargo vehicle inspection system. URL: rknl/eng/products/iDKGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  • S. P. Osipov
    • 1
    Email author
  • E. Yu. Usachev
    • 2
  • S. V. Chakhlov
    • 1
    Email author
  • S. A. Shchetinkin
    • 2
  • S. Song
    • 3
  • G. Zhang
    • 3
  • A. V. Batranin
    • 1
  • O. S. Osipov
    • 4
  1. 1.Tomsk Polytechnic UniversityTomskRussia
  2. 2.MIREA—Russian Technological UniversityMoscowRussia
  3. 3.PowerScan LTDBeijingChina
  4. 4.Solveig MultimediaTomskRussia

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