Methods for detecting and counting nodes in images of crack networks

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...

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Bibliographic Details
Published in:ITM web of conferences 2024, Vol.59, p.2013
Main Author: Rybakov, Alexey
Format: Article
Language:English
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Summary: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.
ISSN:2271-2097
2271-2097
DOI:10.1051/itmconf/20245902013