DeftectNet: Joint loss structured deep adversarial network for thermography defect detecting system

Ruan, Lingfeng, Gao, Bin, Wu, Shichun and Woo, Wai Lok (2020) DeftectNet: Joint loss structured deep adversarial network for thermography defect detecting system. Neurocomputing, 417. pp. 441-457. ISSN 0925-2312

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DeftectNet - Joint Loss Structured Deep Adversarial Network for Thermography Defect Detecting System.pdf - Accepted Version
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Official URL: https://doi.org/10.1016/j.neucom.2020.07.093

Abstract

In this paper, a novel joint loss Generative Adversarial Networks (GAN) framework is proposed for thermography nondestructive testing named Defect-Detection Network (DeftectNet). A new joint loss function that incorporates both the modified GAN loss and penalty loss is proposed. The strategy enables the training process to be more stable and to significantly improve the detection rate. The obtained result shows that the proposed joint loss can better capture the salient features in order to improve the detection accuracy. In order to verify the effectiveness 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/plastic (CFRP) specimens. A comparison experiment has been undertaken to study the proposed method with other current state-of-the-art deep semantic segmentation algorithms. The promising results have been obtained where the performance of the proposed method can achieve end-to-end detection of defects.

Item Type: Article
Uncontrolled Keywords: Generative adversarial network, Loss function, CFRP, Thermography nondestructive testing
Subjects: G400 Computer Science
G500 Information Systems
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 18 Dec 2020 12:49
Last Modified: 18 Dec 2020 13:00
URI: http://nrl.northumbria.ac.uk/id/eprint/45055

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