Alhammad, MuflihAvdelidis, Nicolas PeterIbarra Castanedo, ClementeMaldague, XavierZolotas, ArgyriosTorbali, M. EbubekirGenestc, Marc2022-10-212022-10-212022-10-14Alhammad 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. 3-291768-6733https://doi.org/10.1080/17686733.2022.2126638https://dspace.lib.cranfield.ac.uk/handle/1826/18593The 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/composite materialsinfrared thermographythermal datasetsmachine learningmulti-label classificationArticle