All Issue

2024 Vol.40, Issue 1 Preview Page
Image-Data-Acquisition and Data-Structuring Methods for Tunnel Structure Safety Inspection

터널 구조물 안전점검을 위한 이미지 데이터 취득 및 데이터 구조화 방법

Hyun-Suk Sung1
,
Joon-Sub Koh2*
성 현석1
,
고 준섭2*
1Member, CEO, Dept. of Structural Scanning Research, FMK INC.
2Member, Dept. of Deputy Head, Dept. of Production and Development, FMK INC.
1정회원, ㈜에프엠케이 구조물스캐닝연구부 대표
2정회원, ㈜에프엠케이 생산/개발부 차장
*Corresponding Author
29 February 2024. pp. 15-28
PDF XML Citation
Abstract

This paper proposes a method to acquire image data inside tunnel structures and a method to structure the acquired image data. By improving the conditions by which image data are acquired inside the tunnel structure, high-quality image data can be obtained from area type tunnel scanning. To improve the data acquisition conditions, a longitudinal rail of the tunnel can be installed on the tunnel ceiling, and image data of the entire tunnel structure can be acquired by moving the installed rail. This study identified 0.5 mm cracked simulation lines under a distance condition of 20 m at resolutions of 3,840 × 2,160 and 720 × 480 pixels. In addition, the proposed image-data-structuring method could acquire image data in image tile units. Here, the image data of the tunnel can be structured by substituting the application factors (resolution of the acquired image and the tunnel size) into a relationship equation. In an experiment, the image data of a tunnel with a length of 1,000 m and a width of 20 m were obtained with a minimum overlap rate of 0.02% to 8.36% depending on resolution and precision, and the size of the local coordinate system was found to be (14 × 15) to (36 × 34) pixels.

Keywords
AI/ML
Crack detection
Deep learning
Image processing
Tunnel data structuring

본 연구에서는 터널 구조물 내부 이미지 데이터를 취득하는 방법과 이미지 데이터의 구조화를 위한 방법을 제안하였다. 터널 구조물 내부 이미지 데이터 취득 조건을 개선함으로써 AREA TYPE의 터널 스캐닝에서 고화질의 이미지 데이터를 얻을 수 있다. 데이터 취득 조건을 개선하기 위해 터널 상부에 터널의 길이 방향 레일을 설치하고 설치된 레일을 이동하며 터널 구조물 전체의 이미지 데이터를 취득할 수 있도록 설계하였다. 본 연구는 거리 20m, 해상도 3840×2160 및 해상도 720×480의 조건에서 0.5mm 균열 모사선을 식별하였다. 또한 취득된 이미지 데이터를 이미지 타일 단위로 관리하기 위한 이미지 데이터 구조화 방법을 제안하였다. 터널의 이미지 데이터 구조화를 위해 적용인자(취득 이미지의 해상도와 터널의 크기)를 관계식에 대입하여 터널의 이미지 데이터를 구조화할 수 있다. 실험을 통해 터널 길이 1,000m, 폭 20m 터널의 이미지 데이터는 해상도와 정밀도에 따라 최소중첩률 0.02%에서 8.36% 구해지며 로컬좌표계의 크기는 (14×15)에서 (36×34)로 나타났다.

References
1
American National Standards Institute (2020), Standardization Roadmap For Unmanned Aircraft Systems, Version 2.0. U.S.
2
Seo, S., Ohn, S-Y., Kim, D-H., Kwak, K., and Chung, M. (2020), "A Study on Development of Portable Concrete Crack Measurement Device Using Image Processing Technique and Laser Sensors", Journal of Geosynthetics Society, Vol.19, No.4, pp.41-50.
3
Bae, S.H., Ham, S.W., Lee, H.P., and Kim, D.G. (2022), "Deep Learning based Crack Detection from Tunnel Cement Concrete Lining", Journal of Korean Tunnelling and Underground Space Association, Vol.24, No.6, pp.583-598.
4
Bae, S.J., Jung, W., Chanmrith, S., Kim, C.J., Kim, Y.M., Hong, S.H., Kim, J.G., and Kim, J.Y. (2021), "Performance Evaluation Method of Tunnel Scanner for Lining Crack Detection", Journal of the Society of Disaster Information, Vol.17, No.1, pp.39-52.
5
Cho, S.H., Ga, C., and Yu, K.Y. (2013), "Improvement of Partial Update for the Web Map Tile Service", Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol.31, No.5, pp.365-373. 10.7848/ksgpc.2013.31.5.365
6
Fontana, R., Gambino, M., Greco, M., Pampaloni, E., Pezzati, L., and Scopigno, R. (2003), "High-resolution 3D Digital Models of Artworks", The international Society for Optical Engineering, SPIE October 2003. DOI:10.1117/12.501248. 10.1117/12.501248
7
Ham, S.W., Bae, S.H, Kim, H.Y., Lee, H.P., and Kim, D.G. (2021), "Training a Semantic Segmentation Model for Cracks in the Concrete Lining of Tunnel", Journal of Korean Tunnelling and Underground Space Association, Vol.23, No.6, pp.549-558.
8
Ham, S.W., Bae, S.H, Lee, H.P., and Kim, D.G. (2022), "An Evaluation Methodology for Cement Concrete Lining Crack Segmentation Deep Learning Model", Journal of Korean Tunnelling and Underground Space Association, Vol.24, No.6, pp.513-524.
9
Jee, K. H., Chung, J. M., Hong, S. J., and Kim, S. U. (2004), "A Study of Quality Improvement of the Exterior Inspection Using Tunnel Scanning System", Journal of thr Korean Society for Railway, October 2004, pp.24-29.
10
Kim, A.R., Kim, D.Y., Byun, Y.S., and Lee, S.-W. (2018), "Crack Detection of Concrete Structure Using Deep Learning and Image", Journal of the Korean Geotechnical Society, Vol.34, No.12, pp.145-154.
11
Kim, B.H., Cho, S.J., Chae, H.J., Kim, H.K., and Kang, J.H. (2021), "Development of Crack Detection System for Highway Tunnels using Imaging Device and Deep Learning", Journal of the Korea Institute for Structural Maintenance and Inspection, Vol.25, No.4, pp.65-74.
12
Kim, H., Cho, L., and Kim, K.S. (2019), "Rock Classification Prediction in Tunnel Excavation Using CNN", Journal of the Korean Geotechnical Society, Vol.35, No.9, pp.37-45.
13
Kim, H.H. and Lim, H.D., (2016), "A Basic Study on the Prediction of Collapse of Tunnels Using Artificial Neural Network", Journal of the Korean Geotechnical Society, Vol.32, No.2, pp.5-17. 10.7843/kgs.2016.32.2.5
14
Kim, I. and Lee, C. (2018), "Development of Video Shooting System and Technique Enabling Detection of Micro Cracks in the Tunnel Lining while Driving", Journal of the Korean Society of Hazard Mitigation, Korean Society of Hazard Mitigation, Vol.18, No.5, pp.217-229. 10.9798/KOSHAM.2018.18.5.217
15
Kim, S.J., Kang, Y.J., Choi, I.Y., Hong, K.M., and Ryu, W.J. (2015), "Study on Improvement of Measurement Precision in Digital Image Correlation Measurement Method by Using Subpixel Algorithms", Journal Korean Soc. Precis. Eng., Vol.32, No.12, pp.1039-1047. 10.7736/KSPE.2015.32.12.1039
16
Lee, B. Y., Kim, Y. Y., and Kim, J. K. (2005), "Development of Image Processing for Concrete Surface Cracks by Employing Enhanced Binarization and Shape Analysis Technique", Journal of the Korea Concrete Institute, Vol.17, No.3, pp.361-368. 10.4334/JKCI.2005.17.3.361
17
Lee, G.P., Lim, H.J., and Kim, J.H. (2020), "Availability Evaluation of Automatic Inspection Equipment Using Line Scan Camera for Concrete Lining", Journal of Korean Tunnelling and Underground Space Association, Vol.22, No.6, pp.643-653.
18
Lee, H. B., Kim, J. W., and Jang, I. Y. (2012), "Development of Automatic Crack Detection System for Concrete Structure Using Image Processing Method", Journal of the Korea Institute for Structural Maintenance and Inspection, Vol.16, No.1, pp.64-77. 10.11112/jksmi.2012.16.1.064
19
Lee, H.B., Moon, J.H., and chung, T.Y. (2011), "An Image-Based Remote Snow Height Measurement System using a USN" IEMEK Journal of Embedded Systems and Applications, Vol.6, No.2, pp.76-85.
20
Lee, S.H., Shin, K.J., Kim, H.J., Kim, S.Y., Yoo, C.H., and Eom S. G. (2019), Introduction of Tunnel Crack Measurement Technology Using Image Scanning, Journal of Korean Society of Steel Construction, Vol.31, No.6, pp.42-48.
21
New Energy and Industrial Technology (2018), Guidelines for Evaluating the Performance of Unmanned Aerial Vehicles for Bridge Inspection Ver.1.0. Japan.
22
Oh, j.S. (2013), "Design of a Smart Phone App for Measuring Object Size in a Picture Image", Journal of The Institute of Internet, Broadcasting and Communication, Vol.13, No.5, pp.135-142. 10.7236/JIIBC.2013.13.5.135
23
Seo, S.H., Kim, D.H., and Chung, M.K. (2021), "A Study on Applicability of Smartphone Camera and Lens for Concrete Crack Measurement Using Image Processing Techniques", Journal Korean Geosynthetics Society, Vol.20, No.4, pp.63-71.
24
Sim, S.B., Choi, S.I., Kong, S.M., and Lee, S.W. (2020), "Adversarial Learning for Underground Structure Concrete Crack Detection based on Semi­supervised Semantic Segmentation", Journal of Korean Tunnelling and Underground Space Association, Vol.22, No.5, pp.515-528.
25
Yun, D.Y. and Moon, M.K. (2021), "Development of Kid Height Measurement Application based on Image using Computer Vision", Journal of the KIECS., Vol.16, No.1, pp.117-124.
Information
  • Publisher :The Korean Geotechnical Society
  • Publisher(Ko) :한국지반공학회
  • Journal Title :Journal of the Korean Geotechnical Society
  • Journal Title(Ko) :한국지반공학회 논문집
  • Volume : 40
  • No :1
  • Pages :15-28
  • Received Date : 2023-11-29
  • Revised Date : 2024-01-06
  • Accepted Date : 2024-01-31
  • DOI :https://doi.org/10.7843/kgs.2024.40.1.15
Journal Informaiton Journal of the Korean Geotechnical Society Journal of the Korean Geotechnical Society
Archives
: Vol.23~28 (2007~2012)
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close