Software Implementation of an Algorithm for Automatic Detection of Lineaments and Their Properties in Open-Pit Dumps

This paper presents an algorithm and description of its software implementation for detection of lineaments (ground erosions or cracks) in aerial images of open pits. The proposed approach is based on the apparatus of convolutional neural networks for semantic classification of binarized images of l...

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Veröffentlicht in:	Programming and computer software Jg. 50; H. 1; S. 31 - 41
Hauptverfasser:	Popov, S. E., Potapov, V. P., Zamaraev, R. Y.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Moscow Pleiades Publishing 01.02.2024 Springer Nature B.V
Schlagworte:	Accuracy Aerial photography Algorithms Artificial Intelligence Artificial neural networks Automation Color imagery Computer Science Concrete Cracks Datasets Flaw detection Graph theory Image classification Image resolution Libraries Localization Mathematical models Methods Modules Neural networks Object recognition Operating Systems Pixels Preprocessing Semantics Software Software Engineering Software Engineering/Programming and Operating Systems Source code
ISSN:	0361-7688, 1608-3261
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Abstract	This paper presents an algorithm and description of its software implementation for detection of lineaments (ground erosions or cracks) in aerial images of open pits. The proposed approach is based on the apparatus of convolutional neural networks for semantic classification of binarized images of lineament objects, as well as graph theory for determining the geometric location of linearized lineament objects with subsequent calculation of their lengths and areas. As source data, three-channel RGB images of high-resolution aerial photography (10×10 cm) are used. The software module of the model is logically divided into three levels: preprocessing, detection, and post-processing. The first level implements the preprocessing of input data to form a training sample based on successive transformations of RGB images into binary images by using the OpenCV library. A neural network of the U-Net type, which includes convolutional (Encoder) and scanning (Decoder) blocks, represents the second level of the information model. At this level, automatic detection of objects is implemented. The third level of the model is responsible for calculating their areas and lengths. The result provided by the convolutional neural network is passed to it as input data. The lineament area is calculated by summing the total number of points and multiplying by the pixel size. The lineament length is calculated by linearizing the areal object into a segmented object with node pixels and, then, calculating the lengths between them while taking into account the resolution of the source image. The software module can work with fragments of the source image by combining them. The module is implemented in Python and its source code is available at https://gitlab.ict.sbras.ru/popov/lineaments/-/tree/master/lineaments-cnn .
AbstractList	This paper presents an algorithm and description of its software implementation for detection of lineaments (ground erosions or cracks) in aerial images of open pits. The proposed approach is based on the apparatus of convolutional neural networks for semantic classification of binarized images of lineament objects, as well as graph theory for determining the geometric location of linearized lineament objects with subsequent calculation of their lengths and areas. As source data, three-channel RGB images of high-resolution aerial photography (10×10 cm) are used. The software module of the model is logically divided into three levels: preprocessing, detection, and post-processing. The first level implements the preprocessing of input data to form a training sample based on successive transformations of RGB images into binary images by using the OpenCV library. A neural network of the U-Net type, which includes convolutional (Encoder) and scanning (Decoder) blocks, represents the second level of the information model. At this level, automatic detection of objects is implemented. The third level of the model is responsible for calculating their areas and lengths. The result provided by the convolutional neural network is passed to it as input data. The lineament area is calculated by summing the total number of points and multiplying by the pixel size. The lineament length is calculated by linearizing the areal object into a segmented object with node pixels and, then, calculating the lengths between them while taking into account the resolution of the source image. The software module can work with fragments of the source image by combining them. The module is implemented in Python and its source code is available at https://gitlab.ict.sbras.ru/popov/lineaments/-/tree/master/lineaments-cnn . This paper presents an algorithm and description of its software implementation for detection of lineaments (ground erosions or cracks) in aerial images of open pits. The proposed approach is based on the apparatus of convolutional neural networks for semantic classification of binarized images of lineament objects, as well as graph theory for determining the geometric location of linearized lineament objects with subsequent calculation of their lengths and areas. As source data, three-channel RGB images of high-resolution aerial photography (10×10 cm) are used. The software module of the model is logically divided into three levels: preprocessing, detection, and post-processing. The first level implements the preprocessing of input data to form a training sample based on successive transformations of RGB images into binary images by using the OpenCV library. A neural network of the U-Net type, which includes convolutional (Encoder) and scanning (Decoder) blocks, represents the second level of the information model. At this level, automatic detection of objects is implemented. The third level of the model is responsible for calculating their areas and lengths. The result provided by the convolutional neural network is passed to it as input data. The lineament area is calculated by summing the total number of points and multiplying by the pixel size. The lineament length is calculated by linearizing the areal object into a segmented object with node pixels and, then, calculating the lengths between them while taking into account the resolution of the source image. The software module can work with fragments of the source image by combining them. The module is implemented in Python and its source code is available at https://gitlab.ict.sbras.ru/popov/lineaments/-/tree/master/lineaments-cnn.
Author	Zamaraev, R. Y. Popov, S. E. Potapov, V. P.
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Copyright	Pleiades Publishing, Ltd. 2024. ISSN 0361-7688, Programming and Computer Software, 2024, Vol. 50, No. 1, pp. 31–41. © Pleiades Publishing, Ltd., 2024. Russian Text © The Author(s), 2024, published in Programmirovanie, 2024, Vol. 50, No. 1.
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SubjectTerms	Accuracy Aerial photography Algorithms Artificial Intelligence Artificial neural networks Automation Color imagery Computer Science Concrete Cracks Datasets Flaw detection Graph theory Image classification Image resolution Libraries Localization Mathematical models Methods Modules Neural networks Object recognition Operating Systems Pixels Preprocessing Semantics Software Software Engineering Software Engineering/Programming and Operating Systems Source code
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Title	Software Implementation of an Algorithm for Automatic Detection of Lineaments and Their Properties in Open-Pit Dumps
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