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2024 Vol.26, Issue 6 Preview Page

Research Paper

Performance evaluation of concrete crack detection using deep learning-based super-resolution image reconstruction

딥러닝기반 초해상화 영상 복원을 이용한 콘크리트 균열 탐지의 성능 평가

Jin Kim1
,
Seungbo Shim2
,
Jun-Beom An3
,
Gye-Chun Cho4*
김 진1
,
심 승보2
,
안 준범3
,
조 계춘4*
1Ph.D. Student, Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology
2Senior Researcher, Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology
3Post-Doc Researcher, Applied Science Research Institute, Korea Advanced Institute of Science and Technology
4Professor, Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology
1정회원, 한국과학기술원 건설 및 환경공학과 박사과정
2정회원, 한국건설기술연구원 지반연구본부 수석연구원
3정회원, 한국과학기술원 응용과학연구소 연수연구원
4정회원, 한국과학기술원 건설 및 환경공학과 교수
*Corresponding Author
30 November 2024. pp. 713-728
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Abstract

The maintenance of underground structures, such as concrete tunnels, is essential for the early detection of deterioration phenomena, such as cracks. As the number of aging structures increases, various computer-vision technologies are being applied to efficiently maintain these aging structures. However, images collected in the field can often be low-resolution and low-quality due to various reasons, which can lead to reduced accuracy, especially in crack detection using deep learning. Therefore, this study proposes a method to maintain high detection performance even in low-resolution images by applying super-resolution algorithms. By applying super-resolution algorithms to images with varying proportions of low-quality images, we present an effective method for crack detection and evaluate the utility of several super-resolution algorithms. The experimental results confirmed that the proposed method exhibits stable and improved crack detection performance across various input data conditions. This study demonstrates the potential of super-resolution algorithms in tunnel maintenance and is expected to contribute to accurate maintenance in future real-world tunnel management.

Keywords
Super-resolution
Crack detection
Semantic segmentation
Deep learning
Maintenance

콘크리트 터널과 같은 지하 구조물의 안전한 유지관리는 균열과 같은 열화 현상을 조기에 탐지하는 데 필수적이다. 노후 구조물이 증가함에 따라, 효율적인 안전 관리를 위해 다양한 컴퓨터비전 기술이 구조물 유지 관리에 활용되고 있다. 그러나 현장에서 수집된 영상은 여러 이유로 저해상도 및 저품질로 수집될 수 있으며, 이는 특히 딥러닝을 활용한 균열 탐지에서 정확도 저하로 이어질 수 있다. 따라서, 본 연구에서는 초해상화 알고리즘을 적용하여 저해상도 영상에서도 높은 탐지 성능을 유지할 수 있는 방법을 제안하였다. 다양한 비율의 저해상도 영상을 대상으로 초해상화 알고리즘을 적용하여 균열을 효과적으로 탐지하는 방안을 제시하고, 이를 통해 여러 초해상화 알고리즘의 효용성을 검토하였다. 실험을 통해 제안된 방법이 다양한 입력 데이터 조건에서도 안정적이고 향상된 균열 탐지 성능을 나타냄을 확인하였다. 본 연구는 터널 유지 관리 측면에서 초해상화 알고리즘의 활용 가능성을 보여주며, 향후 실제 터널 유지관리에서 정확한 관리에 기여할 것으로 기대된다.

키워드
초해상화
균열 탐지
의미론적 분할
딥러닝
유지관리
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Information
  • Publisher :Korean Tunneling and Underground Space Association
  • Publisher(Ko) :한국터널지하공간학회
  • Journal Title :Journal of Korean Tunnelling and Underground Space Association
  • Journal Title(Ko) :한국터널지하공간학회 논문집
  • Volume : 26
  • No :6
  • Pages :713-728
  • Received Date : 2024-10-14
  • Revised Date : 2024-10-31
  • Accepted Date : 2024-10-31
  • DOI :https://doi.org/10.9711/KTAJ.2024.26.6.713
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