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.
Highlights
Pipeline (Finite Element Modeling), the proposed method effectively improves the performance of segmentation
Four groups of training databases were created based on the images we COMSOL, and 200 raw thermal images were collected from pulsed thermography expercollected in the previous steps: databases were created based on the images we collected iments
The Probability of Detection (POD) of the databases that merged with synthetic data (B; C) has a better detection capability than any other database without synthetic data (A; D), revealing that merging the raw databases with synthetic data could be a reliable procedure for a deep learning model (Mask-RCNN), enhancing the capability of automatic defect segmentation and identification
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. For the defect identification and detection, several state-of-the-art defect detection algorithms have been proposed in previous literature These included Faster-Region based Convolutional Neural Networks (Faster-RCNN) [4], YOLO-V3 [5], Autoencoders structured neural network [6], and conditional monitoring (CM)-based feature learning methods [7,8]. The Autoencoders structured neural network [6] was proposed as an unsupervised learning method to automatically extract features from intelligent faults This method has made impressive research progress during the thermography data processing. Synthetic data generated with Finite Element Models (FEM) is used during the training process to greatly reduce the high expenses involved in real experiments in infrared thermography.
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