Multi-label classification algorithms for composite materials under infrared thermography testing

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dc.contributor.authorAlhammad, Muflih
dc.contributor.authorAvdelidis, Nicolas Peter
dc.contributor.authorIbarra Castanedo, Clemente
dc.contributor.authorMaldague, Xavier
dc.contributor.authorZolotas, Argyrios
dc.contributor.authorTorbali, M. Ebubekir
dc.contributor.authorGenestc, Marc
dc.date.accessioned2022-10-21T10:27:00Z
dc.date.available2022-10-21T10:27:00Z
dc.date.issued2022-10-14
dc.description.abstractThe key idea in this paper is to propose multi-labels classification algorithms to handle benchmark thermal datasets that are practically associated with different data characteristics and have only one health condition (damaged composite materials). A suggested alternative approach for extracting the statistical contents from the thermal images, is also employed. This approach offers comparable advantages for classifying multi-labelled datasets over more complex methods. Overall scored accuracy of different methods utilised in this approach showed that Random Forest algorithm has a clear higher performance over the others. This investigation is very unique as there has been no similar work published so far. Finally, the results demonstrated in this work provide a new perspective on the inspection of composite materials using Infrared Pulsed Thermography.en_UK
dc.identifier.citationAlhammad M, Avdelidis NP, Ibarra Castenado C, et al., (2024) Multi-label classification algorithms for composite materials under infrared thermography testing. Quantitative InfraRed Thermography Journal, Volume 21, Issue 1, 2024, pp. 3-29en_UK
dc.identifier.issn1768-6733
dc.identifier.urihttps://doi.org/10.1080/17686733.2022.2126638
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18593
dc.language.isoenen_UK
dc.publisherTaylor and Francisen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcomposite materialsen_UK
dc.subjectinfrared thermographyen_UK
dc.subjectthermal datasetsen_UK
dc.subjectmachine learningen_UK
dc.subjectmulti-label classificationen_UK
dc.titleMulti-label classification algorithms for composite materials under infrared thermography testingen_UK
dc.typeArticleen_UK

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