Multi-label classification algorithms for composite materials under infrared thermography testing - Alhammad, Muflih; Avdelidis, Nicolas P.; Ibarra-Castanedo, Clemente; Maldague, Xavier P.; Zolotas, Argyrios; Torbali, Muhammet E.; Genestc, Marc (Taylor & Francis, 2022)
The 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.
Keywords: composite materials; infrared thermography; thermal datasets; machine learning; multi-label classification