Automatic defects segmentation and identification by deep learning algorithm with pulsed thermography: Synthetic and experimental data

Abstract

Infrared thermography is used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces. The application of deep neural networks tends to be a prominent direction in the Infrared Non-Destructive Testing. During the training of the neural network, the Achilles heel is the database. The collection of huge amounts of training data is the high expense task. In Non-Destructive Testing with deep learning, the synthetic data contributing to training in infrared thermography remains unexplored. In this paper, synthetic data from the standard Finite Element Models is combined with experimental data to build repositories with Mask-RCNN to achieve defect segmentation.