Crack Automatic Detection of CCTV Video of Sewer Inspection with Low Resolution

  • Gwanghee Heo
  • Joonryong Jeon
  • Byungjik SonEmail author
Structural Engineering


In South Korea, sewage pipeline exploration devices have been developed using high resolution digital camera of 2 mega-pixels or above. However, most of the devices are less than 300 kilo-pixels due to poverty of the business. Moreover, since 100 kilo-pixels devices are widely used, environment for image processing is very poor. In this study, we adapted very low resolution (240 × 320 = 76,800 pixels) images by which it is difficult to detect cracks. Considering that images of sewers in South Korea have very low resolution, this study selected low resolution images to be investigated. An automatic crack detection technique has been studied using digital image processing technology for low resolution images of sewage pipeline. Authors have developed a program to automatically detect cracks as per eight steps based on MATLAB’s functions. In this study, the third step covers an algorithm developed to find optimal threshold value, and the sixth step deals with algorithm to determine cracks. When we select [1.11:1.29] of zero padding range, exact detection rate is 89.2% and error rate is 4.44%. As the result, in spite of very low-resolution images, the performance of crack detection turned out to be excellent.


image processing low resolution sewer CCTV crack detection user algorithm 


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  1. Mathworks (2014). “Image processing toolbox.” MATLAB R2014b, DOI: 10.5772/63028.Google Scholar
  2. McKim, R. A. and Sinha, S. K. (1999). “Condition assessment of underground sewer pipes using a modified digital image processing paradigm.” Tunnelling and Underground Space Technology, Vol. 14, pp. 29–37, DOI: 10.1016/S0886-7798(00)00021-3.CrossRefGoogle Scholar
  3. Moselhi, O. and Shehab-Eldeen, T. (1999). “Automated detection of surface defects in water and sewer pipes.” Automation in Construction, Vol. 8, No. 5, pp. 581–588, DOI: 10.1016/S0926-5805(99)00007-2.CrossRefGoogle Scholar
  4. Sankarasrinivasan, S., Balasubramanian, E., Karthik, K., Chandrasekar, U., and Rishi, G. (2015). “Health monitoring of civil structures with integrated UAV and image processing system.” Procedia Computer Science, Vol. 54, pp. 508–515, DOI: 10.1016/j.procs.2015.06.058.CrossRefGoogle Scholar
  5. Son, B. J., Jeon, J. R., and Heo, G. H. (2017). “Image processing algorithm for crack detection of sewer with low resolution.” Journal of the Korea Academia-Industrial Cooperation Society, Vol. 18, No. 2, pp. 590–599, DOI: 10.5762/KAIS.2017.18.2.590.Google Scholar
  6. Xu, K., Lxmoore, A. R., and Davies, T. (1998). “Sewer pipe deformation assessment by image analysis of video surveys.” Pattern Recognition, Vol. 31, No. 2, pp. 169–180, DOI: 10.1016/S0031-3203(97)00037-X.CrossRefGoogle Scholar
  7. Yang, M. D. and Su, T. C. (2008). “Automated diagnosis of sewer pipe defects based on machine learning approaches.” Expert Systems with Applications, Vol. 35, No. 3, pp. 1327–1337, DOI: 10.1016/j.eswa.2007.08.013.CrossRefGoogle Scholar
  8. Yang, M. D., Su, T. C., Pan, N. F., and Yang, Y. F. (2011). “Systematic image quality assessment for sewer inspection.” Expert Systems with Applications, Vol. 38, No. 3, pp. 1766–1776, DOI: 10.1016/j.eswa.2010.07.103.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dept. of International Civil & Plant EngineeringKonyang UniversityNonsanKorea

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