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2024 Vol.37, Issue 2 Preview Page

Research Paper

Segmentation of Natural Fine Aggregates in Micro-CT Microstructures of Recycled Aggregates Using Unet-VGG16

Unet-VGG16 모델을 활용한 순환골재 마이크로-CT 미세구조의 천연골재 분할

Sung-Wook Hong1
,
Deokgi Mun2
,
Se-Yun Kim3
,
Tong-Seok Han4*
홍 성욱1
,
문 덕기2
,
김 세윤3
,
한 동석4*
1Graduate Student, Department of Civil and Environmental Engineering, Yonsei University, Seoul, 03722, Korea
2Graduate Student, Department of Civil and Environmental Engineering, Yonsei University, Seoul, 03722, Korea
3Postdoctoral Researcher, Department of Civil and Environmental Engineering, Yonsei University, Seoul, 03722, Korea
4Professor, Department of Civil and Environmental Engineering, Yonsei University, Seoul, 03722, Korea
1연세대학교 건설환경공학과 석박사통합과정
2연세대학교 건설환경공학과 석사과정
3연세대학교 건설환경공학과 박사후연구원
4연세대학교 건설환경공학과 교수
*Corresponding Author
30 April 2024. pp. 143-149
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Abstract

Segmentation of material phases through image analysis is essential for analyzing the microstructure of materials. Micro-CT images exhibit variations in grayscale values depending on the phases constituting the material. Phase segmentation is generally achieved by comparing the grayscale values in the images. In the case of waste concrete used as a recycled aggregate, it is challenging to distinguish between hydrated cement paste and natural aggregates, as these components exhibit similar grayscale values in micro-CT images. In this study, we propose a method for automatically separating the aggregates in concrete, in micro-CT images. Utilizing the Unet-VGG16 deep-learning network, we introduce a technique for segmenting the 2D aggregate images and stacking them to obtain 3D aggregate images. Image filtering is employed to separate aggregate particles from the selected 3D aggregate images. The performance of aggregate segmentation is validated through accuracy, precision, recall, and F1-score assessments.

Keywords
aggregate segmentation
micro-CT image
Unet
recycled aggregate

이미지 분석을 통한 재료의 상 구분은 재료의 미세구조 분석을 위해 필수적이다. 이미지 분석에 주로 사용되는 마이크로-CT 이미지는 대체로 재료를 구성하고 있는 상에 따라 회색조 값이 다르게 나타나므로 이미지의 회색조 값 비교를 통해 상을 구분한다. 순환골재의 고체상은 수화된 시멘트풀과 천연골재로 구분되는데, 시멘트풀과 천연골재는 CT이미지 상에서 유사한 회색조 분포를 보여 상을 구분하기 어렵다. 본 연구에서는 Unet-VGG16 네트워크를 활용하여 순환골재 CT 이미지로부터 천연골재를 분할하는 자동화 방법을 제안하였다. 딥러닝 네트워크를 활용하여 2차원 순환골재 CT 이미지로부터 천연골재 영역을 분할하는 방법과 이를 3차원으로 적층하여 3차원 천연골재 이미지를 얻는 방법을 제시하였다. 선별된 3차원 천연골재 이미지에서 각각의 골재 입자를 분할하기 위해 이미지 필터링을 사용하였다. 골재 영역 분할 성능을 정확도, 정밀도, 재현율 F1 스코어를 통해 검증하였다.

키워드
골재 분할
마이크로-CT 이미지
Unet
순환골재
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Information
  • Publisher :Computational Structural Engineering Institute of Korea
  • Publisher(Ko) :한국전산구조공학회
  • Journal Title :Journal of the Computational Structural Engineering Institute of Korea
  • Journal Title(Ko) :한국전산구조공학회 논문집
  • Volume : 37
  • No :2
  • Pages :143-149
  • Received Date : 2024-03-11
  • Revised Date : 2024-03-21
  • Accepted Date : 2024-03-27
  • DOI :https://doi.org/10.7734/COSEIK.2024.37.2.143
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