Deep Learning Automated Measurements of Expanded Polystyrene Beads Size Using Low-Resolution Micrography

Abstract

The analysis of microscopic characteristics of closed-cell polymeric foams, particularly bead size, is relevant for understanding properties such as thermal insulation, energy absorption, and compressive structural strength of these materials. This study presents an automated method based on Deep Learning models to measure the bead size of Expanded Polystyrene foams in low-resolution micrographs. The results of this approach were compared with manual measurements at two expanded polystyrene foam densities: 8.5 and 24 kg/m³. Hypothesis tests, including Student's t-test, Levene's test, and Mann–Whitney U test, were conducted and showed no significant differences between manual and automatic measurements. Student's t-test and Levene's test indicated that both methods have comparable means and variances, while the Two One-Sided Test confirmed that they were equivalent for bead size measurement. Additionally, the Mann–Whitney U test revealed no differences in medians, and Bland–Altman plot analyses demonstrated no systematic bias between the methods. Taken together, these results suggest that the proposed Deep Learning-based method is a reliable and precise substitute for the manual method in measuring the bead size of expanded polystyrene, making it suitable for practical use in the bead microstructural analysis of expanded polystyrene material.