Garcia Vargas, IagoFernandes, Henrique2025-03-142025-03-142025-02-20Garcia 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. 40-460103-4308https://doi.org/10.22456/2175-2745.143066https://dspace.lib.cranfield.ac.uk/handle/1826/23615For 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.pp. 40-46enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/46 Information and Computing Sciences40 Engineering4016 Materials EngineeringNetworking and Information Technology R&D (NITRD)Machine Learning and Artificial IntelligenceArticle2175-2745565768321