| Issue |
ITM Web Conf.
Volume 59, 2024
II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|
|
|---|---|---|
| Article Number | 02013 | |
| Number of page(s) | 8 | |
| Section | Interdisciplinary Mathematical Modeling and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20245902013 | |
| Published online | 25 January 2024 | |
Methods for detecting and counting nodes in images of crack networks
Astrakhan State University,
Astrakhan,
Russia
* Corresponding author: rybakov alex@mail.ru
The article discusses a technique for segmenting a network of cracks in micrographs and identifying the main elements such as a node, the junction of several cracks, and an edge, the body of the crack itself, to build a model of the network as an undirected graph. Crack segmentation was carried out using two methods: using threshold binarization and applying masks that separate nodes from edges based on morphological characteristics, and a combined method using a convolutional neural network to detect nodes. Such methods make it possible to detect nodes and edges automatically, facilitating the construction of a model and opening up new possibilities in theoretical calculations of the resistance of a network of conductors in transparent conductive coatings.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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