Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

Abstract

In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

Pulsed Thermography Dataset for Training Deep Learning Models

“…In the field of NDE, although there are a number of methods for instance, segmentation in defect detection [18][19][20]. However, the datasets used for these methods are often not publicly available due to internal regulations.…”

Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

“…Grosso et al [66] deploy the Finite Element Model (FEM) for the post-processing of the defect identification in this study to determine the shape of the CUI [66]. FEM is a computation approach that has been used to find approximate solutions to differential equations [67,68]. The FEM subdivides a large system into smaller, simpler parts called finite elements.…”

A deep learning approach based defect visualization in pulsed thermography

“…In this paper [21], synthetic data from the standard finite element models (FEM) are combined with experimental data to build large datasets with mask region based convolutional neural networks (Mask-RCNN), learn essential features of objects of interest and achieve defect segmentation automatically. The results prove the efficiency of adapting inexpensive synthetic data together with the experimental dataset for training the neural networks to obtain an achievable performance from a limited collection of the annotated experimental data of a PT experiment.…”