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Defect shape detection and defect reconstruction in active thermography by means of two-dimensional convolutional neural network as well as spatiotemporal convolutional LSTM network
Quantitative InfraRed Thermography Journal ( IF 2.5 ) Pub Date : 2020-09-13 , DOI: 10.1080/17686733.2020.1810883
David Müller 1, 2 , Udo Netzelmann 1 , Bernd Valeske 1, 2
Affiliation  

ABSTRACT

A neural network (NN) for semantic segmentation (U-Net) was used for the detection of crack-type defects from thermography sequences. For this task, data sequences of forged steel parts were acquired through induction thermography and the corresponding phase images calculated. The results for defect detection were quantitatively evaluated using Intersection over Union (IoU) metric. Further, a combination of 2D convolutional layer as well as LSTM (Long-Short-Term-Memory) is shown, which includes three-dimensional aspects in the form of time dependent and spatial changes and allows a defect shape reconstruction of back wall drillings. Therefore, pulsed thermography sequences were simulated with COMSOL Multiphysics. Finally, the reconstruction results were compared with the ground-truth defect profile using Mean Squared Error (MSE). The approaches provide improvements over conventional methods in non-destructive testing using infrared thermography.



中文翻译:

基于二维卷积神经网络和时空卷积LSTM网络的主动热成像缺陷形状检测和缺陷重建

摘要

用于语义分割 (U-Net) 的神经网络 (NN) 用于检测热成像序列中的裂纹型缺陷。对于该任务,通过感应热成像获取锻钢零件的数据序列并计算相应的相位图像。缺陷检测的结果使用交并比 (IoU) 度量进行了定量评估。此外,显示了 2D 卷积层和 LSTM(长短期记忆)的组合,它包括时间相关和空间变化形式的三维方面,并允许后壁钻孔的缺陷形状重建。因此,使用 COMSOL Multiphysics 模拟了脉冲热成像序列。最后,使用均方误差 (MSE) 将重建结果与真实缺陷轮廓进行比较。

更新日期:2020-09-13
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