Low-rank sparse principal component thermography (sparse-PCT): Comparative assessment on detection of subsurface defects

Abstract

Infrared Non-destructive Testing (IRNDT) applications are unequivocally expanded and portend a commodity to improve the quality of defect detection in different fields such as aviation and industrial methods to arts and archaeology. The proposed approach focuses on the application of low-rank sparse principal component thermography (Sparse-PCT or SPCT) to assess the advantages and drawbacks of the method for non-destructive testing. For benchmarking the approach, two types of infrared image sets are tested: the Square Pulse Thermography (SPT) method for two hybrid composites (carbon and flax fiber reinforced epoxy), and passive infrared test of Bell Tower and the University of L’Aquila (AQ) faculty’s wall infrared sets. The quantitative assessment of the approach is also compared for every method and indicate considerable segmentation performance where other similar approaches were not able to detect the defects. SPCT performance was compared to some popular decomposition methods such as principal component thermography (PCT), candid covariance-free incremental principal component thermography (CCIPCT), non-negative matrix factorization (NMF) using gradient descent (GD) or non-negative least square (NNLS). The comparative results demonstrate the considerable performance while the other methods failed.