On the use of neural networks for the structural characterization of polymeric porous materials
This provides an automatic tool to speed up and enhance reliability for researchers studying porous materials, though it is incremental as it applies existing neural network methods to a specific domain.
The paper tackles the time-consuming and error-prone manual structural characterization of polymeric porous materials by developing a deep-learning-based tool using fine-tuned Mask R-CNN models, achieving very accurate results equivalent to manual methods in seconds.
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.