Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network

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dc.contributor.authorLiu, Haochen
dc.contributor.authorLi, Wenhan
dc.contributor.authorYang, Lichao
dc.contributor.authorDeng, Kailun
dc.contributor.authorZhao, Yifan
dc.date.accessioned2022-07-28T09:56:24Z
dc.date.available2022-07-28T09:56:24Z
dc.date.issued2022-07-25
dc.description.abstractQuantitative defect and damage reconstruction play a critical role in industrial quality management. Accurate defect characterisation in Infrared Thermography (IRT), as one of the widely used Non-Destructive Testing (NDT) techniques, always demands adequate pre-knowledge which poses a challenge to automatic decision-making in maintenance. This paper presents an automatic and accurate defect profile reconstruction method, taking advantage of deep learning Neural Networks (NN). Initially, a fast Finite Element Modelling (FEM) simulation of IRT is introduced for defective specimen simulation. Mask Region-based Convolution NN (Mask-RCNN) is proposed to detect and segment the defect using a single thermal frame. A dataset with a single-type-shape defect is tested to validate the feasibility. Then, a dataset with three mixed shapes of defect is inspected to evaluate the method’s capability on the defect profile reconstruction, where an accuracy over 90% on Intersection over Union (IoU) is achieved. The results are compared with several state-of-the-art of post-processing methods in IRT to demonstrate the superiority at detailed defect corners and edges. This research lays solid evidence that AI deep learning algorithms can be utilised to provide accurate defect profile reconstruction in thermography NDT, which will contribute to the research community in material degradation analysis and structural health monitoring.en_UK
dc.identifier.citationLiu H, Li W, Yang L, et al., (2022) Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network. Neural Computing and Applications, Volume 34, Issue 24, December 2022, pp. 21701–21714en_UK
dc.identifier.issn0941-0643
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07622-6
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18241
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectpulsed thermographyen_UK
dc.subjectfinite element modelingen_UK
dc.subjectdefect reconstructionen_UK
dc.subjectdeep learningen_UK
dc.titleAutomatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural networken_UK
dc.typeArticleen_UK

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