Automatic defects detection in CFRP thermograms, using convolutional neural networks and transfer learning

Recent advancements in the field of Artificial Intelligence can support the post-processing of thermographic data, efficiently, especially for nonlinear or complex thermography scanning routines. This study proposes the implementation of an autonomous/intelligent post-processor that is capable of automatically detecting defects from given thermograms via a Convolutional Neural Networks (CNN) coding, in tandem with a Deep Feed Forward Neural Networks (DFF-NN) algorithm to estimate the defect depth as well. Thus, the proposed NN combination will process (detect and quantify) the defects from acquired thermograms in real-time, and without any human (inspector) intervention. The study shows that employing a pre-trained network, using a relatively small dataset of thermograms for training, can detect and quantify defects in thermographic sequences. In this paper, pre-trained networks with CIFAR-10 and ImageNet databases are used, and followed by a fine-tuning step of the later layers in the network; using a relatively small thermograms dataset. This text will also provide several in-depth studies to compare how transfer learning, state of the art object detection architectures, and the convolutional neural networks influence the performance of the trained post-processing system. The proposed post-processor applied to thermograms obtained from a pulsed-thermography setup testing a Carbon Fiber Reinforced Polymer (CFRP) sample with artificially created sub-surface defects validates the CNN approach.