Szczegóły publikacji
Opis bibliograficzny
Analysis of the possibility of using selected tools and algorithms in the classification and recognition of type of microstructure / Michał Szatkowski, Dorota WILK-KOŁODZIEJCZYK, Krzysztof JAŚKOWIEC, Marcin MAŁYSZA, Adam BITKA, Mirosław GŁOWACKI // Materials [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1944. — 2023 — vol. 16 iss. 21 art. no. 6837, s. 1-13. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 13, Abstr. — Publikacja dostępna online od: 2023-10-24. — D. Wilk-Kołodziejczyk, K. Jaśkowiec, M. Małysza, A. Bitka - dod. afiliacja: Łukasiewicz Research Network, Kraków ; M. Głowacki - dod. afiliacja: Jan Kochanowski University of Kielce
Autorzy (6)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 149767 |
|---|---|
| Data dodania do BaDAP | 2023-12-04 |
| Tekst źródłowy | URL |
| DOI | 10.3390/ma16216837 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Materials |
Abstract
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.