Enhancing defect detection in active infrared thermography using adaptive background suppression techniques

Recent advancements in dimensionality reduction techniques have significantly contributed to the field of active infrared thermography (AIRT) for defect detection, aiding in data processing and feature extraction. Among these techniques, principal component thermography (PCT) and deep autoencoder thermography (DAT) are particularly notable. PCT is based on conventional linear multivariate analysis, while DAT leverages deep learning paradigms to better handle nonlinearity. These methods consolidate defect information from multiple thermograms into a concise set of feature images, enhancing the visibility of subsurface material defects. However, these feature images often suffer from disturbances, particularly non-uniform backgrounds caused by uneven heating in AIRT experiments. Such interferences can obscure defect information, necessitating further post-processing. In our research, we explore the efficacy of Adaptive Iteratively Reweighted Penalized Least Squares (AIR-PELS) as a refinement technique for PCT and DAT, focusing on background suppression. The adaptive iterative weighting with PELS smoothing effectively reduces noise and removes background disturbances. Case studies involving carbon fiber-reinforced polymer samples with inherent defects demonstrate the effectiveness of this post-processing approach.