Identification of 2D colloidal assemblies in images: a threshold processing method versus machine learning

TL;DR

This study compares threshold processing and machine learning methods for identifying 2D colloidal assemblies in images, demonstrating that machine learning achieves significantly higher accuracy, which benefits materials science research.

Contribution

The paper introduces and compares a threshold analysis method and a YOLOv8-based machine learning approach for colloidal assembly identification, highlighting the superior accuracy of the latter.

Findings

Machine learning method achieved 97% accuracy.

Threshold analysis method achieved about 67% accuracy.

The developed algorithms are useful for materials science applications.

Abstract

This paper is devoted to the problem of identification of colloidal assemblies using the example of two-dimensional coatings (monolayer assemblies). Colloidal systems are used in various fields of science and technology, for example, in applications for photonics and functional coatings. The physical properties depend on the morphology of the structure of the colloidal assemblies. Therefore, effective identification of particle assemblies is of interest. The following classification is considered here: isolated particles, dimers, chains and clusters. We have studied and compared two identification methods: image threshold analysis using the OpenCV library and machine learning using the YOLOv8 model as an example. The features and current results of training a neural network model on a dataset specially prepared for this work are described. A comparative characteristic of both methods is given. The best result was shown by the machine learning method (97% accuracy). The threshold processing method showed an accuracy of about 67%. The developed algorithms and software modules may be useful to scientists and engineers working in the field of materials science in the future.