Automatic Defect Detection and Depth Visualization in Mild Steel Sample Using Quadratic Frequency Modulated Thermal Wave Imaging. Issue 1 (February 2021) - Gopi Tilak, V.; Subbarao, G. V.; Vijaya Lakshmi, A.; Suresh, B.
Abstract: Deeper defect detection and depth resolution capabilities of quadratic frequency-modulated optical stimulus became a viable approach for material inspection in active infrared non-destructive testing modality. But the limitations of complex and non-linear analytical models associated with processing techniques propel towards automated defect assessment techniques in infrared thermography. This paper introduces a deep neural network-based automatic defect detection and depth visualization technique in quadratic frequency modulated thermal wave imaging. The neural network classifier uses the modified loss function of a one-class support vector machine to classify defects. The regression network estimates the depth of classified defects. A mild steel specimen with artificial delaminations is numerically modeled and excited by a quadratic frequency-modulated heat flux. The proposed network classification and regression performances are qualitatively assessed using testing time, accuracy, and mean squared error as a figure of merits.