POROZİTE ANALİZİNE DERİN ÖĞRENME YAKLAŞIMI: U-NET İLE DİNAMİK EŞİKLEME

Saim Ervural; Emre Burak Ertuş; Hüseyin Furkan Ceran

doi:10.17780/ksujes.1422819

EN TR

A DEEP LEARNING APPROACH TO POROSITY ANALYSIS: DYNAMIC THRESHOLDING WITH U-NET

Abstract

Many physical methods are used to determine the porosity of materials and these methods are generally applied by employing high cost devices. Also, the existence of variable levels of porosity (micro, meso and macro) in the material affects the type of method to be used. The porosity value can also be calculated using image processing methods, thus saving both time and money. In this study, the numerical porosity value was transferred to the image data as a thresholded image by using ImageJ software during the threshold determination phase in the image processing technique. The generated thresholded label data and the input SEM images were mapped, and the generated dataset was enhanced using the data augmentation methods. The U-Net architecture, a specialised version of convolutional neural networks, was used in the study. The U-Net architecture segmented the microscope images to identify porous regions and calculated porosity values based on the thresholded images of these segments. SEM images of porous materials obtained from the literature were used in the application, and the binary outputs of the porous material according to the porosity values in Archimedes' principle were manually thresholded and recorded as label images. Results were generally correlated with physical measurements and more successful results were obtained than classical image processing methods, thanks to dynamic thresholding using deep learning.

Keywords

POROZİTE ANALİZİNE DERİN ÖĞRENME YAKLAŞIMI: U-NET İLE DİNAMİK EŞİKLEME

Öz

Gözenekli malzemelerin porozite değerinin belirlenmesinde birçok fiziksel yöntem kullanılmaktadır ve bu yöntemler genellikle maliyetli cihazlar marifetiyle uygulanmaktadır. Ayrıca malzemelerde farklı seviyelerde (mikro, mezo ve makro) gözeneklilik bulunması kullanılacak yöntem seçimini de etkilemektedir. Bunun yanında görüntü işleme yöntemleri kullanılarak da porozite değeri hesaplanabilmekte, böylece hem zaman hem de maliyet tasarrufu sağlanabilmektedir. Bu çalışmada görüntü işleme tekniğindeki eşik belirleme aşamasında ImageJ programı kullanılarak sayısal porozitesi eşikli görüntü olarak görüntü verisine aktarılmıştır. Oluşturulan eşikli etiket verileri ile girdi SEM görüntüleri eşlenmiş ve oluşturulan veriseti veri artırma teknikleri kullanılarak genişletilmiştir. Çalışmada evrişimli sinir ağlarının özelleşmiş bir versiyonu olan U-Net mimarisi kullanılmış ve U-Net mimarisi, mikroskop görüntülerini segmentlere ayırarak gözenekli bölgeleri belirlemiş ve bu segmentlerin eşiklenmiş görüntülerine dayalı olarak gözeneklilik değerleri hesaplanmıştır. Uygulamada literatürden elde edilen gözenekli malzemelerin SEM görüntüleri kullanılmış, etiket görüntüleri olarak ise Arşimet prensibindeki porozite değerlerine göre gözenekli malzemenin ikili çıktıları manuel olarak eşiklenerek kaydedilmiştir. Çalışma sonucunda genel olarak fiziki ölçümlerle korelasyon sağlamış ve derin öğrenmeden faydalanılan dinamik eşikleme sayesinde klasik görüntü işleme yöntemlerine göre daha başarılı sonuçlar elde edilmiştir.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Görüntü İşleme , Derin Öğrenme , Malzeme Bilimi ve Teknolojileri , Hesaplamalı Malzeme Bilimleri

Bölüm

Araştırma Makalesi

Yazarlar

Saim Ervural ^*
0000-0003-4104-1928
Türkiye

Emre Burak Ertuş
0000-0002-3897-2409
Türkiye

Hüseyin Furkan Ceran
0009-0006-2169-4680
Türkiye

Yayımlanma Tarihi

3 Eylül 2024

Gönderilme Tarihi

23 Ocak 2024

Kabul Tarihi

28 Ağustos 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1422819

IZ

https://izlik.org/JA75YA45TR

Kaynak Göster

RIS / Bibtex

APA

Ervural, S., Ertuş, E. B., & Ceran, H. F. (2024). POROZİTE ANALİZİNE DERİN ÖĞRENME YAKLAŞIMI: U-NET İLE DİNAMİK EŞİKLEME. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 1069-1077. https://doi.org/10.17780/ksujes.1422819