Browse > Article
http://dx.doi.org/10.7734/COSEIK.2018.31.3.147

Line Laser Image Processing for Automated Crack Detection of Concrete Structures  

Kim, Junhee (Department of Architectural Engineering, Dankook University)
Shin, Yoon-Soo (Department of Architectural Engineering, Dankook University)
Min, Kyung-Won (Department of Architectural Engineering, Dankook University)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.31, no.3, 2018 , pp. 147-153 More about this Journal
Abstract
Cracking in concrete structure must be examined according to appropriate methods, to ensure structural serviceability and to prevent structural deterioration, since cracks opened wide for a long time expedite corrosion of rebar. A site investigation is conducted in a regular basis to monitor structural deterioration by tracking growing cracks. However, the visual inspection are labor intensive. and judgment are subject. To overcome the limit of the on-site visual investigation image processing for identifying the cracks of concrete structures by analyzing 2D images has been developed. This study develops a unique 3D technique utilizing a line laser and its projection image onto concrete surfaces. Automated process of crack detection is developed by the algorithms of automatizing crack map generation and image data acquisition. Performance of the developed method is experimentally evaluated.
Keywords
crack detection; concrete structure; line laser; image processing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Adgikari, R.S., Moselhi, O., Bagchi, A. (2014) Image-based Retrieval of Concrete Crack Properties, Autom. Constr., 39, pp.180-194.   DOI
2 Baum, L.E. (1972) An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Finite State Markov Chains, Ann. Math. Stat., 41, pp.164-171.
3 Cha, Y.J., Choi, W., Buyukozturk, O. (2017) Deep Learning-Based Crack Damage Detection using Convolutional Neural Networks, Comput.-Aided Civil & Infrastruct. Eng., 32(5), pp.361-378.   DOI
4 Chen, L., Shao, Y., Jan, H., Huang, C., Tien, Y. (2006) Measuring System for Cracks in Concrete using Multitemporal Images, J. Surv. Eng., 2, pp.77-82.
5 Cho, H.W., Yoon, H.J., Park, J.J. (2014) An Experimental Study on Crack Recognition Characteristics of Concrete Structure based on Image Analysis according to Illuminance and Measurement Distance, J. Korean Soc. Hazard Mitig., 14, pp.85-91.
6 Kang, J.M., Oh, Y.C., Um, D.Y. (2002) The Crack Information Acquisition of Concrete Object by Digital Image Processing, J. Korean Soc. Civil Eng., 5, pp.10-18.
7 Kim, K.B., Cho, J.H., Ahn, S.H. (2005) A Technique for Image Processing of Concrete Surface Cracks, J. Korea Inst. Marit. Inform. & Commun. Sci., 9(7), pp.1575-1581.
8 Kwon, W.M. (2001) Emerging Trends in 3D Digital Heritage, Broadcasting and Media Magazine, Volume 3, pp.88-97.
9 Lee, H.B., Kim, J.W., Jang, I.Y. (2012) Development of Automatic Crack Detection System for Concrete Structure using Image Processing Method, Korea Inst. Struct. Maint. Insp., 16(1), pp.64-77.
10 Otsu, N. (1975) A Threshold Selection Method from Gray-level Histograms, Autom., 11, pp.23-27.
11 Park, S.W., Yoon, S.H. (2002) A Comparative Study on the Repair Performance of Concrete Crack.
12 Paul, D., Harry, H., Clive, F. (2002) An Operational Application of Automatic Feature Extraction, Meas. Crack. Concr. Struct., 17, pp.453-464.
13 Rafael, C.G., Paul, A.W. (1987) Digital Image Processing, Addison-Wesley, p.503.
14 Takafumi, N., Junji, Y. (2012) Concrete Crack Detection by Multiple Sequential Image Filtering, Comput.-Aided Civil & Infrastruct. Eng., 27(1) pp.29-47.   DOI