Image-Based Size Analysis of Agglomerated and Partially Sintered Particles via Convolutional Neural Networks

TL;DR

This paper introduces a deep learning method using convolutional neural networks trained solely on synthetic images for automated analysis of primary particle sizes in agglomerated and sintered particles, outperforming existing methods.

Contribution

A novel CNN-based approach trained on synthetic data for particle size analysis that eliminates manual parameter tuning and annotation, achieving high accuracy on real samples.

Findings

Outperforms state-of-the-art methods like Hough transformation and ImageJ ParticleSizer.

Achieves human-like performance in particle size detection.

Works effectively across various sintering degrees and image conditions.

Abstract

There is a high demand for fully automated methods for the analysis of primary particle size distributions of agglomerated, sintered or occluded primary particles, due to their impact on material properties. Therefore, a novel, deep learning-based, method for the detection of such primary particles was proposed and tested, which renders a manual tuning of analysis parameters unnecessary. As a specialty, the training of the utilized convolutional neural networks was carried out using only synthetic images, thereby avoiding the laborious task of manual annotation and increasing the ground truth quality. Nevertheless, the proposed method performs excellent on real world samples of sintered silica nanoparticles with various sintering degrees and varying image conditions. In a direct comparison, the proposed method clearly outperforms two state-of-the-art methods for automated image-based particle size analysis (Hough transformation and the ImageJ ParticleSizer plug-in), thereby attaining human-like performance.