Composite material defect segmentation using deep learning models and infrared thermography
| dc.contributor.author | Garcia Vargas, Iago | |
| dc.contributor.author | Fernandes, Henrique | |
| dc.date.accessioned | 2025-03-14T16:12:48Z | |
| dc.date.available | 2025-03-14T16:12:48Z | |
| dc.date.freetoread | 2025-03-14 | |
| dc.date.issued | 2025-02-20 | |
| dc.date.pubOnline | 2025-02-20 | |
| dc.description.abstract | For non-destructive assessment, the segmentation of infrared thermographic images of carbon fiber composites is a critical task in material characterization and quality assessment. This study focuses on applying image processing techniques, particularly adaptive thresholding, alongside neural network models such as U-Net and DeepLabv3 for infrared image segmentation tasks. An experimental analysis was conducted on these networks to compare their performance in segmenting artificial defects from infrared images of a carbon-fibre reinforced polymer sample. The performance of these models was evaluated based on the F1-Score and Intersection over Union (IoU) metrics. The findings reveal that DeepLabv3 demonstrates superior results and efficiency in segmenting patterns of infrared images, achieving an F1-Score of 0.94 and an IoU of 0.74, showcasing its potential for advanced material analysis and quality control. | |
| dc.description.journalName | Revista de Informática Teórica e Aplicada | |
| dc.description.sponsorship | This study was financed in part by the Coordenacao de Aper-feicoamento de Pessoal de Nivel Superior – Brasil (CAPES) –Finance Code 001 and by the National Council for Scientificand Technological Development - Brazil (CNPq) – Finance Codes 407140/2021-2 and 312530/2023-4. | |
| dc.format.extent | pp. 40-46 | |
| dc.identifier.citation | Garcia Vargas I, Fernandes H. (2025) Composite material defect segmentation using deep learning models and infrared thermography. Revista de Informática Teórica e Aplicada, Volume 32, Issue 1, February 2025, pp. 40-46 | |
| dc.identifier.eissn | 2175-2745 | |
| dc.identifier.elementsID | 565768 | |
| dc.identifier.issn | 0103-4308 | |
| dc.identifier.issueNo | 1 | |
| dc.identifier.uri | https://doi.org/10.22456/2175-2745.143066 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/23615 | |
| dc.identifier.volumeNo | 32 | |
| dc.language.iso | en | |
| dc.publisher | Universidade Federal do Rio Grande do Sul | |
| dc.publisher.uri | https://seer.ufrgs.br/index.php/rita/article/view/143066 | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | 46 Information and Computing Sciences | |
| dc.subject | 40 Engineering | |
| dc.subject | 4016 Materials Engineering | |
| dc.subject | Networking and Information Technology R&D (NITRD) | |
| dc.subject | Machine Learning and Artificial Intelligence | |
| dc.title | Composite material defect segmentation using deep learning models and infrared thermography | |
| dc.type | Article | |
| dcterms.dateAccepted | 2024-12-02 |