Masked Sequence Autoencoding for Enhanced Defect Visualization in Active Infrared Thermography

Active infrared thermography (AIRT) became a crucial tool in aerospace non-destructive testing (NDT), enabling the detection of hidden defects and anomalies in materials by capturing thermal responses over time. In AIRT, autoencoders are widely used to enhance defect detection by reducing the dimensionality of thermal data and improving the signal-to-noise ratio. However, traditional AIRT autoencoders often struggle to disentangle subtle defect features from dominant background responses, leading to suboptimal defect analysis under varying material and inspection conditions. To overcome this challenge, this work proposes a Masked CNN-Attention Autoencoder (AIRT-Masked-CAAE) that integrates convolutional feature extraction with attention mechanisms to capture both local thermal patterns and global contextual dependencies. The AIRT-Masked-CAAE introduces a masked sequence autoencoding strategy, where the network learns to infer missing thermal responses from surrounding contextual cues, while suppressing background redundancy. In addition, the proposed masked sequence autoencoding approach enables training on only a subset of the thermal sequence, while providing generalizable latent representations and reducing training time by a factor of 30. The AIRT-Masked-CAAE framework was evaluated using specimens made of PVC, CFRP, and PLA. The results demonstrate that the AIRT-Masked-CAAE surpasses state-of-the-art AIRT autoencoders in terms of contrast, signal-to-noise ratio (SNR), and metrics based on neural networks.

PCA-Guided Autoencoding for Structured Dimensionality Reduction in Active Infrared Thermography

Active Infrared thermography (AIRT) is a widely adopted non-destructive testing (NDT) technique for detecting subsurface anomalies in industrial components. Due to the high dimensionality of AIRT data, current approaches employ non-linear autoencoders (AEs) for dimensionality reduction. However, the latent space learned by AIRT AEs lacks structure, limiting their effectiveness in downstream defect characterization tasks. To address this limitation, this paper proposes a principal component analysis guided (PCA-guided) autoencoding framework for structured dimensionality reduction to capture intricate, non-linear features in thermographic signals while enforcing a structured latent space. A novel loss function, PCA distillation loss, is introduced to guide AIRT AEs to align the latent representation with structured PCA components while capturing the intricate, non-linear patterns in thermographic signals. To evaluate the utility of the learned, structured latent space, we propose a neural network-based evaluation metric that assesses its suitability for defect characterization. Experimental results show that the proposed PCA-guided AE outperforms state-of-the-art dimensionality reduction methods on PVC, CFRP, and PLA samples in terms of contrast, signal-to-noise ratio (SNR), and neural network-based metrics.