A Study on Development of Portable Concrete Crack Measurement Device Using Image Processing Technique and Laser Sensors

Seunghwan Seo; Syng-Yup Ohn; Dong-Hyun Kim; Kiseok Kwak; Moonkyung Chung

doi:10.12814/jkgss.2020.19.4.041

All Issue

2020 Vol.19, Issue 4 Preview Page Next Page

Research Article

A Study on Development of Portable Concrete Crack Measurement Device Using Image Processing Technique and Laser Sensors 이미지 처리기법 및 레이저 센서를 이용한 휴대용 콘크리트 균열 측정 장치 개발에 관한 연구

31 December 2020. pp. 41-50

PDF XML

Abstract

Since cracks in concrete structures expedite corrosion of reinforced concrete over a long period of time, regular on-site inspections are essential to ensure structural usability and prevent degradation. Most of the safety inspections of facilities rely on visual inspection with naked eye, so cost and time consuming are severe, and the reliability of results differs depending on the inspector. In this study, a portable measuring device that can be used for safety diagnosis and maintenance was developed as a device that measures the width and length of concrete cracks through image analysis of cracks photographed with a camera. This device captures the cracks found within a close distance (3 m), and accurately calculates the unit pixel size by laser distance measurement, and automatically calculates the crack length and width with the image processing algorithm developed in this study. In measurement results using the crack image applied to the experiment, the measurement of the length of a 0.3 mm crack within a distance of 3 m was possible with a range of about 10% error. The crack width showed a tendency to be overestimated by detecting surrounding pixels due to vibration and blurring effect during the binarization process, but it could be effectively corrected by applying the crack width reduction function.

Keywords

Concrete crack

Image processing

Portable device

Laser sensor

콘크리트 구조물의 균열은 장기간 지속 시 철근의 부식을 촉진시키므로 구조적 사용성을 보장하고 열화를 방지하기 위해 정기적인 현장 점검이 필수적이다. 대부분의 시설물 안전점검은 육안 검사에 의존하고 있어 비용과 시간 소모가 심하고 점검자에 따라 결과의 신뢰도 차이가 발생한다. 본 연구에서는 카메라로 촬영된 균열의 이미지 분석을 통해 콘크리트 균열의 폭과 길이를 측정하는 장치로서 안전진단 및 유지관리에 사용할 수 있는 휴대용 측정 장치를 개발하였다. 이 장치는 측정자가 육안으로 발견한 균열을 가까운 거리 (3m) 이내에서 촬영하고 레이저 거리측정으로 단위 픽셀크기를 정확히 산정하여, 본 연구에서 개발한 이미지 처리 알고리즘으로 균열 길이와 폭을 자동으로 산정할 수 있다. 측정결과 실험에 적용한 균열 이미지를 이용하여 3m 거리 이내에서 0.3mm 균열의 길이 측정은 약 10% 오차 범위에서 측정 가능하였다. 균열 폭의 경우 이진화 과정에서 진동 및 Blurring에 의한 주변픽셀을 검출해 과대평가되는 경향을 나타내었으나, 균열 폭 감소함수를 적용하여 효과적으로 보정할 수 있었다.

References

Doihara, T., Hirono, K. and Oda, K. (1993), “Crack measuring system based on Hierarchical Image Processing technique.” International Archives of Photogrammetry and Remote Sensing, Vol.29, No.5, pp.155-159.

Jee, K. H. (2014), “Advanced structure damage measurement technology - crack image processing technique,” KICT Brief technical report, pp.11-13. (in Korean)

Kim, A. R., Kim, D., Byun, Y. S. and Lee, S. W. (2018b). “Crack detection of concrete structure using deep learning and image processing method in geotechnical engineering,” Journal of the korean geotechnical society, Vol.34, No.12, pp.145-154. (in Korean)

Kim, J., Shin, Y. S. and Min, K. W. (2018a), “Line laser image processing for automated crack detection of concrete structures,” J. Comput. Struct. Eng. Ins. Korea, Vol.31, No.3, pp.147-153. (in Korean) 10.7734/COSEIK.2018.31.3.147

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. (in Korean) 10.4334/JKCI.2005.17.3.361

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. (in Korean) 10.11112/jksmi.2012.16.1.064

Liu Y., Yao J., Lu X., Xie R. and Li L. (2019). “DeepCrack: A deep hierarchical feature learning architecture for crack segmentation.” Neurocomputing, Vol.338, pp.139-153. 10.1016/j.neucom.2019.01.036

Otsu, N. (1979) A threshold selection method from gray-level histogram. IEEE Transactions on Systems, MAN, and Cybernetics, vol. SMC-9, No.1, pp.62-66. 10.1109/TSMC.1979.4310076

Sonka, M., Hlavac, V. and Boyle R. (2015). “Image processing, analysis, and machine vision.” 4th edition of CL Engineering, Boston, MA.

Soycan, A. and Soycan, M. (2019) “Perspective correction of building facade images for architectual applications.” Engineering Science and Technology and International Journal, Vol.22, pp.697-705. 10.1016/j.jestch.2018.12.012

Tanaka, N. and Uematsu, K. (1998) “A crack detection method in road surface images using morphology,” Proceeding of IAPR Workshop on Machine Vision Application, Chiba, Japan, pp.154-157.

Zhang, W., Zhang, Z., Qi, D. and Liu, Y. (2014) “Automatic crack detection and classification method for subway tunnel safety monitoring.” Sensors, 14, pp.19307-19328. 10.3390/s14101930725325337PMC4239952

Information

Publisher :Korean Geosythetics Society
Publisher(Ko) :한국지반신소재학회
Journal Title :Journal of the Korean Geosynthetics Society
Journal Title(Ko) :한국지반신소재학회 논문집
Volume : 19
No :4
Pages :41-50
Received Date : 2020-11-12
Revised Date : 2020-12-02
Accepted Date : 2020-12-05
DOI :https://doi.org/10.12814/jkgss.2020.19.4.041

Journal of the Korean Geosynthetics Society ISSN:2508-2876(Print) 2287-9528(Online) 한국지반신소재학회 논문집

All Issue