DEEP AND HANDCRAFTED FEATURE FUSION FOR AUTOMATIC DEFECT DETECTION IN QUADRATIC FREQUENCY MODULATED THERMAL WAVE IMAGING
Recent advancements of non-destructive testing and evaluation (NDT&E) reached the fourth revolution with machine learning, artificial intelligence, and the internet of things as key enablers in parallel with industry 4.0. Nevertheless, Active thermography (AT) is a non-contact, whole field, safe, remote, cost-efficient, and widely used NDT technique for subsurface anomaly detection. In AT, the automatic defect detection is modelled as object localization and semantic segmentation in thermograms. This paper presents a feature fusion network that fuses the global features extracted using a deep neural network (DNN) with the deep features extracted using a convolutional neural network (CNN). A set of handcrafted time-domain statistical and frequency domain features of thermal profiles are given to the DNN sub-network whereas, the CNN sub-network is fed with the thermal profiles in the feature fusion network. Experimentation is carried out over carbon fiber reinforced polymer (CFRP) sample with artificially drilled flat bottom holes excited by quadratic frequency-modulated optical stimulus. Experimental results showed that the feature fusion enhanced the defect detection capability compared to the local networks with a significant increment in signal-to-noise ratio, accuracy, and F-score.