RT info:eu-repo/semantics/article
T1 On the use of neural networks for the structural characterization of polymeric porous materials
A1 Torre Ordás, Jorge
A1 Barroso Solares, Suset
A1 Rodríguez Pérez, Miguel Ángel
A1 Pinto Sanz, Javier
K1 Polimeros y polimerización
K1 Deep learning
K1 Mask R–CNN
K1 Automatic
K1 Aprendizaje profundo
K1 Automático
K1 Máscara R-CNN
K1 2301.15 Análisis de Polímeros
AB The structural characterization is an essential task in the study of porous materials. To achieve reliable results, it requires to evaluate images with hundreds of pores. Current methods require large time amounts and are subjected to human errors and subjectivity. A completely automatic tool would not only speed up the process but also enhance its reliability and reproducibility. Therefore, the main objective of this article is the study of a deep-learning-based technique for the structural characterization of porous materials, through the use of a convolutional neural network. Several fine-tuned Mask R–CNN models are evaluated using different training configurations in four separate datasets each composed of numerous SEM images of diverse polymeric porous materials: closed-pore extruded polystyrene (XPS), polyurethane (PU), and poly(methyl methacrylate) (PMMA), and open-pore PU. Results prove the tool capable of providing very accurate results, equivalent to those achieved by time-consuming manual methods, in a matter of seconds.
PB Elsevier
SN 0032-3861
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/66062
UL https://uvadoc.uva.es/handle/10324/66062
LA eng
NO Polymer, 2024, vol. 291, 126597
NO Producción Científica
DS UVaDOC
RD 27-jul-2024