Nano-particles size measurement based on semantic segmentation via convolution neural network

Abstract

Automatic recognition and size measurement of nanoparticles in SEM images have been an evolving field of study within the material informatics domain. These methods have been typically assessed using traditional image processing techniques. This paper presents a robust and fully automated pipeline for all researchers, particularly those in chemical engineering, based on image processing techniques and convolution neural networks. We evaluated our method on a dataset of 1,701 2D particle images, employing a three-fold split for training, validation, and testing. The proposed approach integrates semantic segmentation models (U-Net, LinkNet) with size measurement algorithms, achieving high accuracy even in the presence of noise or blurriness. Our results demonstrate the method effectiveness in precisely estimating particle sizes, with a Pearson correlation coefficient of 0.9633 and mean absolute error of 1.287e-06. The method’s robustness against poor-quality images and its fully automated procedures make it a valuable tool for researches in SEM images field.