https://doi.org/10.1140/epje/s10189-026-00560-4
Research – Soft Matter
Identification of 2D colloidal assemblies in images: a threshold processing method versus machine learning
Mathematical Modeling Lab, Astrakhan Tatishchev State University, 20a Tatishchev str., 414056, Astrakhan City, Astrakhan Region, Russia
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Received:
29
October
2025
Accepted:
21
January
2026
Published online:
17
February
2026
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.
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© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2026
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
