Temporal and spatial deep learning network for infrared thermal defect detection

Qin Luo, Bin Gao*, Wai Lok Woo, Yang Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

102 Citations (Scopus)
94 Downloads (Pure)


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.

Original languageEnglish
Article number102164
JournalNDT and E International
Early online date30 Aug 2019
Publication statusPublished - 1 Dec 2019


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