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

Alhammad M., Avdelidis N. P., Castanedo C. I., Maldague X., Zolotas A., Torbali E., ...More

QUANTITATIVE INFRARED THERMOGRAPHY JOURNAL, vol.21, no.1, pp.3-29, 2024 (SCI-Expanded, Scopus)

Abstract

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.