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Temporal and spatial deep learning network for infrared thermal defect detection
شبکه یادگیری عمیق زمانی و مکانی برای تشخیص نقص حرارتی مادون قرمز-2019 Most common types of defects for composite are debond and delamination. It is difficult to detect the inner
defects on a complex shaped specimen by using conventional optical thermography nondestructive testing (NDT)
methods. In this paper, a hybrid of spatial and temporal deep learning architecture for automatic thermography
defects detection is proposed. The integration of cross network learning strategy has the capability to significantly
minimize the uneven illumination and enhance the detection rate. The probability of detection (POD) has
been derived to measure the detection results and this is coupled with comparison studies to verify the efficacy of
the proposed method. The results show that visual geometry group-Unet (VGG-Unet) cross learning structure can
significantly improve the contrast between the defective and non-defective regions. In addition, investigation of
different feature extraction methods in which embedded in deep learning is conducted to optimize the learning
structure. To investigate the efficacy and robustness of the proposed method, experimental studies have been
carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer (CFRP)
specimens. Keywords: Deep learning | Segmentation | Thermography defect detection | Nondestructive testing |
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