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해체와 구성을 이용한 다중 스케일 균열 검출

Multi-scale crack detection using decomposition and composition

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초록

In this paper, we propose a multi-scale crack detection method. This method uses decomposition, composition, and shape properties. It is based on morphology algorithm, crack features. We use a morphology operator which extracts patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. However, morphology methods using only one structure element could detect only fixed width crack. Thus, we use decomposition and composition methods. We use a decimation method for decomposition. After decomposition and morphology operation, we get edge images given by binary values. Our method calculates values of properties such as the number of pixels and the maximum length of the segmented region. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed multi-scale crack detection method has better results than those of existing detection methods.

Abstract

In this paper, we propose a multi-scale crack detection method. This method uses decomposition, composition, and shape properties. It is based on morphology algorithm, crack features. We use a morphology operator which extracts patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. However, morphology methods using only one structure element could detect only fixed width crack. Thus, we use decomposition and composition methods. We use a decimation method for decomposition. After decomposition and morphology operation, we get edge images given by binary values. Our method calculates values of properties such as the number of pixels and the maximum length of the segmented region. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed multi-scale crack detection method has better results than those of existing detection methods.

참고문헌 (14)

  1. N. Otsu, "A threshold selection method from gray-level histograms", IEEE Trans. Systems, Man, and Cybernetics, 9(1), pp.62~66 (1979)
  2. A. Ito, "Accurate extraction and measurement of fine cracks", Processing of IECON'02, pp.2202~2207 (2002)
  3. Y. Fujita, "A method for crack detection on a concrete structure", ICPR'06, pp.901~904 (2006)
  4. R. S. Adhikari, "Image-based retrieval of concrete crack properties", Journal of the International Society for Gerontechnology, 11(2), pp.315~321 (2012)
  5. Salembier, P, "Comparison of some morphological segmentation algorithms based on contrast enhancement. Application to automatic defect detection", Proc. of the EUSIPCO-90 Fifth European Signal Processing Conference, pp.833~836 (1990)
  6. S. Iyer, "A robust approach for automatic detection and segmentation of cracks in underground pipeline images", Image and Vision Computing, 23(10), pp.921~933 (2005)
  7. S. K. Sinha, "Segmentation of buried concrete pipe images", Automation in Construction, 15(1), pp.47~57 (2006)
  8. S. K. Sinha, "Automated detection of cracks in buried concrete pipe images", Automation in Construction, 15(1), pp.58~72 (2006)
  9. T. Yamaguchi, "Image based crack detection for real concrete surfaces", Trans. on Electrical and Electronic Engineering, IEEJ Trans 2008, 3(1), pp.128~135 (2008)
  10. T. Yamaguchi, "Practical image measurement of crack width for real concrete structure", Electronics and Communications in Japan, 92(10), pp.605~614 (2009)
  11. 차성원, "웨이브렛 상세 영역 변환을 이용한 임펄스 잡음 제거", (사)디지털산업정보학회 논문지, 4(2), pp.75~80 (2008), (사)디지털산업정보학회
  12. A. Hashemi, "Adaptive noise variance estimation in BayesShrink", IEEE Signal Processing Letters, 17(1), pp.12~15 (2010)
  13. 조범석, "적응적 필터링을 이용한 가우시안 잡음 예측", (사)디지털산업정보학회 논문지, 8(4), pp.13~18 (2012), (사)디지털산업정보학회
  14. 구윤모, "잡음 영상에서의 에지 검출", (사)디지털산업정보학회 논문지, 8(3), pp.41~48 (2012), (사)디지털산업정보학회

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