| Download | - View final version: Multi-label classification algorithms for composite materials under infrared thermography testing (PDF, 17.7 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1080/17686733.2022.2126638 |
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| Author | Search for: Alhammad, Muflih1; Search for: Avdelidis, Nicolas Peter1ORCID identifier: https://orcid.org/0000-0003-1314-0603; Search for: Ibarra Castanedo, Clemente2; Search for: Maldague, Xavier2; Search for: Zolotas, Argyrios1; Search for: Torbali, Ebubekir1; Search for: Genest, Marc3 |
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| Affiliation | - Cranfield University
- Université Laval
- National Research Council Canada. Aerospace
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| Format | Text, Article |
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| Subject | composite materials; infrared thermography; thermal datasets; machine learning; multi-label classification |
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| 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. |
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| Publication date | 2022-10-14 |
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| Publisher | Taylor & Francis |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 62ed138d-71fc-45ac-9361-c3daa5dfc1bb |
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| Record created | 2023-08-30 |
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| Record modified | 2023-09-18 |
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