Temporal and spatial deep learning network for infrared thermal defect detection

Luo, Qin, Gao, Bin, Woo, Wai Lok and Yang, Yang (2019) Temporal and spatial deep learning network for infrared thermal defect detection. NDT & E International, 108. p. 102164. ISSN 0963-8695

Temporal Spatial Deep Learning Infrared Thermal Detection_author version.pdf - Accepted Version
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Official URL: https://doi.org/10.1016/j.ndteint.2019.102164


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.

Item Type: Article
Uncontrolled Keywords: Deep learning, Segmentation, Thermography defect detection, Nondestructive testing
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 14 Oct 2019 10:38
Last Modified: 31 Jul 2021 12:31
URI: http://nrl.northumbria.ac.uk/id/eprint/41092

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