Concrete crack detection using shape properties
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this paper, we propose a concrete crack detection method using shape properties. It is based on morphology algorithm and crack features. We assume that an input image is contaminated by various noises. Thus, we use a morphology operator and extract 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. Also, it is robust to noisy environment. The proposed algorithm classifies the segmented image into crack and background using shape properties of crack. This method calculates values of properties such as the number of pixels and the maximum length of the segmented region. Also, pixel counts of clusters are considered. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed crack detection method has better results than those by existing detection methods.
Abstract
In this paper, we propose a concrete crack detection method using shape properties. It is based on morphology algorithm and crack features. We assume that an input image is contaminated by various noises. Thus, we use a morphology operator and extract 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. Also, it is robust to noisy environment. The proposed algorithm classifies the segmented image into crack and background using shape properties of crack. This method calculates values of properties such as the number of pixels and the maximum length of the segmented region. Also, pixel counts of clusters are considered. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed crack detection method has better results than those by existing detection methods.
참고문헌 (12)
- A. Ito, "Accurate extraction and measurement of fine cracks", Processing of IECON’02, pp.2202~2207 (2002)
- Y. Fujita, "A method for crack detection on a concrete structure", ICPR’06, pp.901~904 (2006)
- N. Otsu, "A threshold selection method from gray-level histograms", IEEE Trans. Systems, Man, and Cybernetics, 9(1), pp.62~66 (1979)
- R. S. Adhikari, "Image-based retrieval of concrete crack properties", Journal of the International Society for Gerontechnology, 11(2), pp.315~321 (2012)
- 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)
- S. Iyer, "A robust approach for automatic detection and segmentation of cracks in underground pipeline images", Image and Vision Computing, 23, pp.921~933 (2005)
- S. K. Sinha, "Segmentation of buried concrete pipe images", Automation in Construction, 15, pp.47~57 (2006)
- S. K. Sinha, "Automated detection of cracks in buried concrete pipe images", Automation in Construction, 15, pp.58~72 (2006)
- T. Yamaguchi, "Image based crack detection for real concrete surfaces", Trans. on Electrical and Electronic Engineering, 3, pp.128~135 (2008)
- T. Yamaguchi, "Practical image measurement of crack width for real concrete structure", Electronics and Communications in Japan, 92(10), pp.605~614 (2009)
- 차성원, "웨이브렛 상세 영역 변환을 이용한 임펄스 잡음 제거", (사)디지털산업정보학회 논문지, 4(2), pp.75~80 (2008), (사)디지털산업정보학회
- 조범석, "적응적 필터링을 이용한 가우시안 잡음 예측", (사)디지털산업정보학회 논문지, 8(4), pp.13~18 (2012), (사)디지털산업정보학회