Image-Based Size Analysis of Agglomerated and Partially Sintered Particles via Convolutional Neural Networks
This provides an automated solution for material scientists analyzing particle size distributions, which is incremental but improves upon existing methods.
The researchers tackled the problem of automated size analysis of agglomerated and sintered particles by proposing a deep learning-based method that uses synthetic images for training, achieving human-like performance and outperforming two state-of-the-art methods.
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.