A Novel Deep Neural Network-Based Framework for Subsurface Analysis in Non-Stationary Thermal Wave Imaging - Swapna, M. N.; Suman, M.; Prasanthi, Y. N.; Ghali, V. S.; Mulaveesala, R. (Sensing and imaging, 2025)
Enhanced depth resolution and deeper depth scanning are the most pivotal challenges in thermal wave imaging-based non-destructive evaluation. In the recent past, sweep frequency stimulation and traditional signal processing have been successful in analyzing non-linearity, which has resulted in a high-dimensional, noise-embedded thermal response and restricted detectability. It is difficult to extract high-level features from high-dimensional data that are meant to improve fault signatures in a lower-dimensional space by encoding. This work proposes a constrained shallow convolutional autoencoder to address these shortcomings of conventional autoencoders. This approach enforces correlated encoded data, orthogonality between encoder and decoder weights, and unit norm length of these weights to boost performance. By implementing the constraints, the proposed autoencoder aims to deliver superior performance and enhance defect visibility. Experiments conducted on mild steel specimens using quadratic frequency modulated thermal wave imaging validate the applicability of this modality, and the results clearly demonstrate an edge over the existing state-of-the-art modalities as quantified in terms of defect signal-to-noise ratio (SNR).