An Automated Particle Size Analysis Method for SEM Images of Powder Coating Particles

Abstract

The particle size of powder coatings is an important factor affecting flow and fluidization performance, which in turn determines the quality of the coating. Powder coating particles, such as boron nitride, titanium dioxide, barium titanate, niobium oxide, and tungsten oxide, can be seen in SEM images as circular or polygonal shapes, with irregular edges and sizes, as well as aggregation and stacking between them. This paper introduces a novel method of automatic particle identification and size analysis applied in SEM images for the investigation of coating quality. Firstly, a fast gradient segmentation algorithm (Split–Merge) is utilized to automatically generate samples through determining the adhesive particles and particle groups. The samples are then manually checked and corrected to further segment the particles in order to form the dataset. Finally, the YOLOv5, a mature target detection algorithm, is used to train the labeled data in order to produce a multi-target detection recognition model. The model can be applied to identify more pictures of the same substance, and the particles’ long and short axes are estimated using the elliptical coverage of the particles within the identified image boundary box. Experiment results from four kinds of powders suggest that this method can provide automatic particle size analysis for industrial SEM particle images.