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Temporal and spatial deep learning network for infrared thermal defect detection

Lookup NU author(s): Professor Bin Gao, Dr Wai Lok Woo, Yang Yang

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Abstract

© 2019 Elsevier LtdMost 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.

Publication metadata

Author(s): Luo Q, Gao B, Woo WL, Yang Y

Publication type: Article

Publication status: Published

Journal: NDT & E International

Year: 2019

Volume: 108

Print publication date: 01/12/2019

Online publication date: 30/08/2019

Acceptance date: 28/08/2019

ISSN (print): 0963-8695

ISSN (electronic): 1879-1174

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.ndteint.2019.102164

DOI: 10.1016/j.ndteint.2019.102164

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