Characterization of subsurface anomalies in non-stationary thermal wave imaging using convolutional neural networks

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Abstract An important development in the field of thermographic study is the application of convolutional neural networks (CNNs) to characterize subsurface abnormalities using quadratic frequency-modulated thermal wave imaging. This technology greatly improves the accuracy of identifying subsurface flaws by utilizing deep learning techniques, which makes it very useful for a variety of industrial applications cantered on material integrity evaluation. In this study, we present a new post-processing modality based on regression that is intended to efficiently characterize subsurface anomalies. Through experimental experiments involving carbon fibre reinforced polymers (CFRP) and glass fibre reinforced polymers (GFRP), our methodology has been rigorously confirmed. The purpose of the studies was to demonstrate the ability of our method to visualize subsurface structures, with a focus on precisely measuring their sizes. We evaluate our method's efficacy by contrasting the outcomes with current methodologies, employing signal-to-noise ratio (SNR) as a crucial performance parameter. This assessment proves our method's superiority in offering more trustworthy and lucid insights about underlying structures. In the end, our research adds to the expanding body of knowledge about non-destructive testing and has encouraging ramifications for material science and engineering applications in the future.