Szczegóły publikacji
Opis bibliograficzny
Deep learning for porous media classification based on micro-CT images / Małgorzata Charytanowicz, Piotr A. KOWALSKI, Szymon ŁUKASIK, Piotr KULCZYCKI, Henryk Czachor // W: IJCNN 2022 [Dokument elektroniczny] : International Joint Conference on Neural Networks : Padua, Italy, 18–23 July 2022 : proceedings / IEEE. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2022. — ( Proceedings of ... International Joint Conference on Neural Networks ; ISSN 2161-4393 ). — Konferencja zorganizowana w ramach IEEE World Congress on Computational Intelligence (IEEE WCCI 2022). — e-ISBN: 978-1-7281-8671-9. — S. [1–8]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [7–8], Abstr. — Publikacja dostępna online od: 2022-09-30. — P. A. Kowalski, S. Łukasik, P. Kulczycki - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences, Warsaw
Autorzy (5)
- Charytanowicz Małgorzata
- AGHKowalski Piotr Andrzej
- AGHŁukasik Szymon
- AGHKulczycki Piotr
- Czachor Henryk
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 142995 |
|---|---|
| Data dodania do BaDAP | 2022-10-29 |
| Tekst źródłowy | URL |
| DOI | 10.1109/IJCNN55064.2022.9891899 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Joint Conference on Neural Networks 2022 |
| Czasopismo/seria | Proceedings of ... International Joint Conference on Neural Networks |
Abstract
Deep convolutional neural networks have strong data structure mining ability and can be successfully applied to facilitate the feature detection process when applied to porous media analysis. One of the encountered limitations is the lack of large data when applying deep learning algorithms. This work proposes a novel approach to overcome this problem by generating highly representative datasets based on real CT-images. The method uses an original morphological concept for the determination of pore size distribution to provide input data that is to be fed to a designed neural network. The generated data was employed for the classification of soil aggregates that differ in their pore size distribution. The image classification results were achieved by exploiting well-known pre-trained deep learning models: VGG-16, ResNet50, InceptionV3, Dense-Net121, and MobileNet. We applied k-fold cross-validation for k equal to five to validate the results. The average accuracy for the validating data achieved for the MobileNet, VGG-16, InceptionV3 and DenseNet121 ranged from 90% to 95% and were 5% higher compared to ResNet50. The deep learning approach has demonstrated great promise for analyzing very complex and irregular structures based on micro-CT images.
