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Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks

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Abstract

For defense applications, rapid X-ray inspection of propellant samples is essential for the identification and assessment of defects. Automation of this process using artificial intelligence is possible by properly training a neural network model. Convolution Neural Networks (CNNs) have recently demonstrated excellent success in both the tasks of image recognition and localisation using an adequate amount of data. In real-world, it’s not an easy task to produce the correct amount of experimental data required for the deep neural network to operate. In this work, we propose a method for producing synthetic radiographic data that is supported by ray tracing based radiographic simulations for the deep learning algorithms to automatically detect anomaly in X-ray images. The simulation results, which are then supplemented by noise extracted from the experimental data, show a good comparison with the measurements. This Simulation assisted Automatic Defect Recognition (Sim-ADR) system simultaneously perform defect detection and defect instance segmentation. The accuracy of the defect detection system is more than 87% on a testing set included 416 images.

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Acknowledgements

This study was funded by Armaments Research Board (ARMREB) and supported by High Energy Material Research Laboratory (HEMRL). We would like to thank Anirudha Sane and Deepak Patil from HEMRL, Pune for helping us in procuring the experimental data.

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Authors and Affiliations

  1. Center for Non-destructive Evaluation, Indian Institute of Technology Madras, Chennai, India

    Dhruv Gamdha, Sreedhar Unnikrishnakurup & Krishnan Balasubramaniam

  2. Dhvani Research, Chennai, India

    K. J. Jyothir Rose, M. Surekha & Padma Purushothaman

  3. High Energy Material Research Laboratory, Pune, India

    Bikash Ghose

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  1. Dhruv Gamdha
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  2. Sreedhar Unnikrishnakurup
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  3. K. J. Jyothir Rose
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  7. Krishnan Balasubramaniam
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Correspondence to Dhruv Gamdha.

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This document is the results of the research project funded by Armaments Research Board (ARMREB) in collaboration with the High Energy Material Research Laboratory (HEMRL).

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Gamdha, D., Unnikrishnakurup, S., Rose, K.J.J. et al. Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks. J Nondestruct Eval 40, 18 (2021). https://doi.org/10.1007/s10921-021-00750-4

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