Decision tree-based subsurface analysis using Barker coded thermal wave imaging

•An automated machine learning based subsurface analysis.•Avoidance of human intervention and improved detectability.•Ever first ML's use for BCTWI. Non-stationary thermal wave imaging is emerging as a reliable alternative procedure due to its depth scanning and defect detection capabilities to...

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Bibliographic Details
Published in:Infrared physics & technology 2020-09, Vol.109, p.103380, Article 103380
Main Authors: Parvez M, Muzammil, Shanmugam, J., Ghali, V.S.
Format: Article
Language:English
Subjects:
Barker coded thermal wave imaging
Decision tree
Non-stationary thermal wave imaging
Thermal wave imaging
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Summary:•An automated machine learning based subsurface analysis.•Avoidance of human intervention and improved detectability.•Ever first ML's use for BCTWI. Non-stationary thermal wave imaging is emerging as a reliable alternative procedure due to its depth scanning and defect detection capabilities to assess the integrity of the materials. This investigation proposes a Decision Tree based anomaly detection in Barker Coded Thermal Wave Imaging (BCTWI). It facilitates automatic detection and visualization of anomalies with better spatial accuracy in a single view to help even an inexperienced personnel to easily assess subsurface features.
ISSN:1350-4495
1879-0275
DOI:10.1016/j.infrared.2020.103380