Sparse-Data Deep Learning Strategies for Radiographic Non-Destructive Testing

Alvarez, Jacqueline; Henderson, Keith; Aufderheide, Maurice B.; Gallagher, Brian; Marcia, Roummel F.; Jiang, Ming

doi:10.1080/09349847.2025.2519070

Sparse-Data Deep Learning Strategies for Radiographic Non-Destructive Testing

Journal Article · Fri Jul 04 00:00:00 EDT 2025 · Research in Nondestructive Evaluation

DOI:https://doi.org/10.1080/09349847.2025.2519070· OSTI ID:2586667

Alvarez, Jacqueline ^[1]; Henderson, Keith ^[2]; Aufderheide, Maurice B. ^[2]; Gallagher, Brian ^[2]; Marcia, Roummel F. ^[1]; Jiang, Ming ^[2]

University of California, Merced, CA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Radiography is an imaging technique used in a variety of applications, such as medical diagnosis, airport security, and nondestructive testing. We present a deep learning system for extracting information from radiographic images. We perform various prediction tasks using our system, including material classification and regression on the dimensions of a given object that is being radiographed. Our system is designed to address the sparse-data issue for radiographic nondestructive testing applications. It uses a radiographic simulation tool for synthetic data augmentation, and it uses transfer learning with a pre-trained convolutional neural network model. Using this system, our preliminary results indicate that the object geometry regression task saw an improvement of 70% in the R-squared value when using a multi-regime model. In addition, we increase the performance of the object material classification tasks by utilizing data from different imaging systems. In particular, using neutron imaging improved the material classification accuracy by 20% when compared to x-ray imaging.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 2586667

Report Number(s):: LLNL--JRNL-868701

Journal Information:: Research in Nondestructive Evaluation, Journal Name: Research in Nondestructive Evaluation; ISSN 1432-2110; ISSN 0934-9847

Publisher:: Informa UK LimitedCopyright Statement

Country of Publication:: United States

Language:: English

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Radiography
convolutional neural networks
deep learning
nondestructive testing

Sparse-Data Deep Learning Strategies for Radiographic Non-Destructive Testing

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References (29)

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