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Deep learning-based sustainable subsurface anomaly detection in Barker-coded thermal wave imaging

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Abstract

Deep learning-based sustainable subsurface for anomaly detection in different materials is an objective to improve the reliability of thermographic inspection. This article aims to describe a method that uses Barker-coded thermal wave imaging to identify subsurface anomalies in materials. The novelty of the proposed methodology is to detect smaller defects at a higher depth even on a fully corroded sample of mild steel. Experiments were carried out with different kinds of samples like mild steel and glass fiber reinforced plastic (GFRP). Various commonly used modern post-processing techniques are applied alongside the proposed techniques for detecting subsurface anomalies. Subsurface anomalies visualized using the proposed deep learning method give better visualization and results when compared to that of other approaches. In addition to it, region-based active contour segmentation-based detection is also proposed for the GFRP sample. This study results in a high signal-to-noise ratio (SNR) of value 108 dB; the least error in defect size is nearly 0.01% using full width at half maximum (FWHM), and the aspect ratio is nearly 1 for the proposed convolutional neural network (CNN)-based processing approach.

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Authors and Affiliations

  1. Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India

    Muzammil Parvez, Venkata Subba Rao Ghali & Aravindhan Alagarsamy

  2. School of ECE, Reva University, Bengaluru, Karnataka, India

    Ali Baig Mohammad

  3. Department of ECE, DVR & Dr HS MIC College of Technology, Kanchikacherla, Andhra Pradesh, India

    Gampa Chandra Sekhar Yadav

  4. Department of ECE, KSRM College of Engineering, Kadapa, Andhra Pradesh, India

    Gopi Tilak Vesala

  5. Department of ECE, Amrita Sai Institute of Science and Technology, Paritala, Andhra Pradesh, India

    Atala Vijaya Lakshmi

  6. Dilkap Research Institute of Engineering and Management Studies, Neral, Maharashtra, 410101, India

    Sivasubramanian Palanisamy

  7. Department FWSD, University of Thessaly, 43100, Karditsa, Greece

    John Kechagias

  8. School of Science and Technology, Università di Camerino, 62032, Camerino, Italy

    Carlo Santulli

Authors
  1. Muzammil Parvez
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  2. Ali Baig Mohammad
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  3. Venkata Subba Rao Ghali
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  4. Gampa Chandra Sekhar Yadav
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  5. Gopi Tilak Vesala
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  6. Atala Vijaya Lakshmi
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  7. Aravindhan Alagarsamy
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  8. Sivasubramanian Palanisamy
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  9. John Kechagias
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  10. Carlo Santulli
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Contributions

All authors contributed to the study’s conception and design. Material preparation and data collection were carried out by Muzammil Parvez and Ali Baig Mohammad. Data analysis was performed by Venkata Subba Rao Ghali, Gampa Chandra Sekhar Yadav, Gopi Tilak Vesala, Atala Vijaya Lakshmi, and Aravindhan Alagarsamy, with interpretations and valuable comments provided by John Kechiagias and Carlo Santulli. The first draft of the manuscript was written by Sivasubramanian Palanisamy, Muzammil Parvez, and Venkata Subba Rao Ghali. The edited draft was also provided by John Kechiagias and Carlo Santulli. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Muzammil Parvez, Sivasubramanian Palanisamy or Carlo Santulli.

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Parvez, M., Mohammad, A.B., Ghali, V.S.R. et al. Deep learning-based sustainable subsurface anomaly detection in Barker-coded thermal wave imaging. Int J Adv Manuf Technol 127, 3625–3635 (2023). https://doi.org/10.1007/s00170-023-11753-y

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  • DOI: https://doi.org/10.1007/s00170-023-11753-y

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