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

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.

Automated summary

This article describes a method that uses Barker-coded thermal wave imaging to identify subsurface anomalies in materials. The proposed methodology can detect smaller defects at a higher depth even on a fully corroded sample of mild steel. Experiments were conducted with various samples and both deep learning and active contour segmentation techniques were used for anomaly detection. The results show that the proposed deep learning method gives better visualization and analysis of subsurface anomalies compared to other approaches.