About some approaches to problem of metals and alloys microstructures classification based on neural network technologies

Klestov, Roman; Klyuev, A.V.; Stolbov, V. Yu.

doi:10.2991/aime-18.2018.56

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Materials 2023, 16, x FOR PEER REVIEW 2 of 14 comparison of various methods used in the classification problem [4][5][6][7][8][9][10][11]. When solving this problem, it is also worth thoroughly analyzing the principles of operation of tools and algorithms based on artificial intelligence [12][13][14][15][16][17][18].…”

Section: Methodsmentioning

confidence: 99%

“…The first is a standard convolutional network used for object detection-in this case, it was decided to use the effective RetinaNet architecture. Convolutional neural network comparison of various methods used in the classification problem [4][5][6][7][8][9][10][11]. When solving this problem, it is also worth thoroughly analyzing the principles of operation of tools and algorithms based on artificial intelligence [12][13][14][15][16][17][18].…”

Section: Methodsmentioning

confidence: 99%

“…The problem of classifying Images of microstructures is the subject of many studies described and presented in the subsequent literature [1][2][3][4]. These works highlight the tools and techniques used in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Their analysis allows us to determine the direction of development of tools to solve this problem. These works are of a review nature [1,2] or focus on a specific problem defined in materials engineering [3] or a comparison of various methods used in the classification problem [4][5][6][7][8][9][10][11]. When solving this problem, it is also worth thoroughly analyzing the principles of operation of tools and algorithms based on artificial intelligence [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of the Possibility of Using Selected Tools and Algorithms in the Classification and Recognition of Type of Microstructure

Szatkowski,

Wilk-Kołodziejczyk,

Jaśkowiec

et al. 2023

Materials

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The aim of this research was to develop a solution based on existing methods and tools that would allow the automatic classification of selected images of cast iron microstructures. As part of the work, solutions based on artificial intelligence were tested and modified. Their task is to assign a specific class in the analyzed microstructure images. In the analyzed set, the examined samples appear in various zoom levels, photo sizes and colors. As is known, the components of the microstructure are different. In the examined photo, there does not have to be only one type of precipitate in each photo that indicates the correct microstructure of the same type of alloy, different shapes may appear in different amounts. This article also addresses the issue of data preparation. In order to isolate one type of structure element, the possibilities of using methods such as HOG (histogram of oriented gradients) and thresholding (the image was transformed into black objects on a white background) were checked. In order to avoid the slow preparation of training data, our solution was proposed to facilitate the labeling of data for training. The HOG algorithm combined with SVM and random forest were used for the classification process. In order to compare the effectiveness of the operation, the Faster R-CNN and Mask R-CNN algorithms were also used. The results obtained from the classifiers were compared to the microstructure assessment performed by experts.

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