Journal of the Korea institute for structural maintenance and inspection (한국구조물진단유지관리공학회 논문집)

Korea Institute for Structural Maintenance Inspection (한국구조물진단유지관리공학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of Maximum Crack Width Using Histogram Analysis in Concrete Structures

히스토그램 분석을 이용한 콘크리트 구조물의 최대 균열 폭 평가

  • Received : 2019.09.06
  • Accepted : 2019.12.30
  • Published : 2019.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of present study is to assess the maximum width of the surface cracks using the histogram analysis of image processing techniques in concrete structures. For this purpose, the concrete crack image is acquired by the camera. The image is Grayscale coded and Binary coded. After Binary coded image is Dilate and Erode coded, the image is then recognized as separated objects by applying Labeling techniques. Over time, dust and stains may occur naturally on the surface of concrete. The crack image of concrete may include shadows and reflections by lighting depending on a surrounding conditions. In general, concrete cracks occur in a continuous pattern and noise of image appears in the form of shot noises. Bilateral Blurring and Adaptive Threshold apply to the Grayscale image to eliminate these effects. The remaining noises are removed by the object area ratio to the Labeled area. The maximum numbers of pixels and its positions in the crack objects without noises are calculated in x-direction and y-direction by Histogram analysis. The widths of the crack are estimated by trigonometric ratio at the positions of the pixels maximum numbers for the Labeled objects. Finally, the maximum crack width estimated by the proposed method is compared to the crack width measured with the crack gauge. The proposed method by the present study may increase the reliability for the estimation of maximum crack width using image processing techniques in concrete surface images.

본 연구의 목적은 영상 처리 기법의 히스토그램 분석을 이용하여 콘크리트 구조물 표면의 최대 균열 폭을 평가하는 것이다. 이를 위하여 콘크리트 표면 균열에 대한 영상을 촬영하고, 촬영된 영상을 회색 영상 및 이진화 영상으로 변환하였다. 이진화된 영상은 팽창과 침식이 적용된 후 레이블링을 통하여 분리된 객체로 인식된다. 콘크리트 표면은 시간이 경과함에 따라 먼지와 얼룩 등이 발생될 수 있으며, 촬영 조건에 따라 그림자 및 조명 반사가 포함될 수 있다. 또한, 콘크리트 균열은 연속적인 형상으로 발생되는 반면에 잡음은 점의 형태로 나타난다. 이러한 영향을 제거하기 위하여 이진화 과정은 양방향 블러와 적응적 경계를 적용하였으며, 레이블링된 영역에 대하여 면적비를 통한 잡음 제거를 수행하였다. 잡음이 제거된 각각의 균열 객체는 히스토그램 분석을 통하여 x축과 y축에 대한 최대값 및 그 위치가 연산되고, 분리된 객체에 대한 각각의 최대값 위치에서 삼각비를 통하여 균열 폭을 평가하게 된다. 제안된 방법에 의해 평가된 최대 균열 폭은 균열 게이지에 의해 계측된 값과 비교 분석되었다. 본 연구에 의해서 제안된 방법은 콘크리트 표면 영상에 대한 균열 폭 평가에 신뢰성을 향상 시킬 수 있을 것이다.

Keywords

References

  1. Ammouche, A., Breysse, D., Hornain, H., Didry, O., and Marchand, J. (2000), A New Image Analysis Technique for the Quantitative Assessment of Microcracks in Cement-based Materials, Cem. Concr. Res., 30, 25-35. https://doi.org/10.1016/S0008-8846(99)00212-4
  2. Bradski, G. R., and Kaehler, A. (2008), Learning OpenCV Computer Vision with the OpenCV Library, O'Reilly, 109-143.
  3. Byun, T. B., Kim, J. H., and Kim, H. S. (2006), The Recognition of Crack Detection Using Difference Image Analysis Method based on Morphology, Journal of the Korea Institute of Information and Communication Engineering, 10(1), 197-205 (in Korean).
  4. De Schutter, G. (2002), Advanced Monitoring of Cracked Structures Using Video Microscope and Automated Image Analysis, NDT&E Int., 35, 209-212. https://doi.org/10.1016/S0963-8695(01)00042-1
  5. Gonzalez, R. C., and Woods, R. E. (2010), Digital Image Processing, Third Edition, Pearson Prentice Hall, 760-785.
  6. Her, J. Y., Kim, K. R., Lim, E. K., Ahn, S. H., and Kim, K. B. (2006), A Length and Width Extraction of Concrete Surface Cracks using Image Processing Techniques, Proceedings of the Korea Institute of Maritime Information & Communication Sciences, 346-351 (in Korean).
  7. Jung, C. H., Oh, A. S., and Kim, K. B. (2007), Extraction and Analysis of Concrete Surface Cracks, Proceedings of the Korea Multimedia Society Conference, 46-52, (in Korean).
  8. Kim, H. J., Ahn, E. J., Cho, S. J., Shin, M. S., and Sim, S. H. (2017), Comparative Analysis of Image Binarization Methods for Crack Identification in Concrete Structures, Cement and Concrete Research, 99, 53-61. https://doi.org/10.1016/j.cemconres.2017.04.018
  9. Kim, K. B., Cho, J. H., and Ahn, S. H. (2005), A Technique for Image Processing of Concrete Surface Cracks, Journal of the Korea Institute of Maritime Information & Communication Sciences, 9(7), 1575-1581, (in Korean).
  10. 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, KSMI., 16(1), 64-77 (in Korean). https://doi.org/10.11112/jksmi.2012.16.1.064
  11. Litorowicz, A. (2006), Identification and Quantification of Cracks in Concrete by Optical Fluorescent Microscopy, Cem. Concr. Res. 36, 1508-1515. https://doi.org/10.1016/j.cemconres.2006.05.011
  12. Nishikawa, T., Yoshida, J., Sugiyama, T., and Fujino, Y. (2012), Concrete Crack Detection by Multiple Sequential Image Filtering, Comput-Aided Civ. Infrastruct. Eng., 27, 29-47. https://doi.org/10.1111/j.1467-8667.2011.00716.x
  13. Talab, A., Huang, Z., Xi, F., and Haiming, L. (2016), Detection Crack in Image Using Otsu Method and Multiple Filtering in Image Processing Techniques, Optik, 127(3), 1030-1033. https://doi.org/10.1016/j.ijleo.2015.09.147
  14. Tomasi, C., and Manduchi, R. (1998), Bilateral Filtering for Gray and Color Images, Sixth International Conference on Computer Vision, 839-846.