Abstract:
Infrared thermography (IRT) is extensively used as non-destructive testing and evaluation (NDT&E) technique to
inspect and characterize various solid materials and structures. In this paper, an emergent optical thermography
NDT&E technique i.e. frequency modulated thermal wave imaging (FMTWI) has been used for the inspection of
mild steel sample embedded with artificially constructed flat bottom circular holes of the same diameter at
various depth. This article proposes an independent component analysis (ICA) to process the FMTWI image
sequence for detecting the subsurface defects of mild steel sample. To evaluate the effectiveness of defect detection capability of the proposed method, the conventional data processing techniques viz. phase analysis, pulse
compression and principal component analysis (PCA) have been compared with ICA. The signal-to-noise (SNR)
has been considered to characterize and quantify the defect detectability and compared with conventional postprocessing techniques to validate the efficiency of the proposed approach. The obtained results provide an
insight into the robustness of the ICA approach for defect detection. Furthermore, an active contour model-based
object detection technique has been employed for identification, localization, and extraction of the shape of the
defects.
