Two-stream convolutional neural network for non-destructive subsurface defect detection via similarity comparison of lock-in thermography signals

Active infrared thermography is a safe, fast, and low-cost solution for subsurface defects inspection, providing quality control in many industrial production tasks. In this paper, we explore deep learning-based approaches to analyze lock-in thermography image sequences for non-destructive testing and evaluation (NDT&E) of subsurface defects. Different from most existing Convolutional Neural Network (CNN) models that directly classify individual regions/pixels as defective and non-defective ones, we present a novel two-stream CNN architecture to extract/compare features in a pair of 1D thermal signal sequences for accurate classification/differentiation of defective and non-defective regions. In this manner, we can significantly increase the size of the training data by pairing two individually captured 1D thermal signals, thereby greatly easing the requirement for collecting a large number of thermal sequences of specimens with defects to train deep CNN models. Moreover, we experimentally investigate a number of network alternatives, identifying the optimal fusion scheme/stage for differentiating the thermal behaviors of defective and non-defective regions. Experimental results demonstrate that our proposed method, directly learning how to construct feature representations from a large number of real-captured thermal signal pairs, outperforms the well-established lock-in thermography data processing techniques on specimens made of different materials and at various excitation frequencies.