Abstract
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.
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