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

机译：脉冲热处理深度学习算法自动缺陷和识别：合成和实验数据

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摘要

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.
机译：在工业生产领域的质量评估（QE）中，红外热成像（IRT）是用于评估复合材料的最重要的技术之一，由于低成本，快速检测的大表面和安全性。深度神经网络的应用趋于在IRT非破坏性测试（NDT）中的突出方向。在神经网络的培训期间，Achilles脚跟是大型数据库的必要性。巨额培训数据的集合是高费用任务。在NDT深度学习中，有助于在红外热成像训练的合成数据仍然相对未开发。在本文中，来自标准有限元模型的合成数据与实验数据组合，以构建基于掩模区域的卷积神经网络（Mask-RCNN）来构建存储库以加强神经网络，从学习感兴趣对象的基本特征和实现缺陷分割自动地。这些结果表明，适应具有一定数量的实验数据库的廉价合成数据合并的可能性，以训练神经网络，以便从有限的收集现实世界实际热成像实验的注释实验数据收集中实现引人注目的性能。

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