Advertisement

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

  • Xiangyang Ye
  • Jian’e ZuoEmail author
  • Ruohan Li
  • Yajiao Wang
  • Lili Gan
  • Zhonghan Yu
  • Xiaoqing Hu
Research Article
  • 8 Downloads

Abstract

Closed circuit television (CCTV) systems are widely used to inspect sewer pipe conditions. During the diagnosis process, the manual diagnosis of defects is time consuming, labor intensive and error prone. To assist inspectors in diagnosing sewer pipe defects on CCTV inspection images, this paper presents an image recognition algorithm that applies features extraction and machine learning approaches. An algorithm of image recognition techniques, including Hu invariant moment, texture features, lateral Fourier transform and Daubechies (DBn) wavelet transform, was used to describe the features of defects, and support vector machines were used to classify sewer pipe defects. According to the inspection results, seven defects were defined; the diagnostic system was applied to a sewer pipe system in a southern city of China, and 28,760 m of sewer pipes were inspected. The results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%. The overall accuracy reached 84.1%. The diagnosing accuracy depended on the number of the training samples, and four fitting curves were applied to fit the data. According to this paper, the logarithmic fitting curve presents the highest coefficient of determination of 0.882, and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.

Keywords

Sewer pipe defects Defect diagnosing Image recognition Multi-features extraction Support vector machine 

Notes

Acknowledgements

This work was supported by the Mega-Projects of Science Research forWater Environment Improvement (Nos. 2011ZX07301-002 and 2017ZX07103-007). We would like to express our gratitude to the people from the local sewer management authority who provided the necessary help and convenience during the cooperation.

References

  1. Canadian Standards Association (2010). Canadian Standards Association Technical Guide-PLUS 4012: Visual Inspection of Sewer Pipe. Canadian Standards Association, Toronto, ON, CanadaGoogle Scholar
  2. Dang L M, Hassan S I, Im S, Mehmood I, Moon H (2018). Utilizing text recognition for the defects extraction in sewers CCTV inspection videos. Computers in Industry, 99: 99–109CrossRefGoogle Scholar
  3. Dirksen J, Clemens F H L R, Korving H, Cherqui F, Le Gauffre P, Ertl T, Plihal H, Müller K, Snaterse C TM(2013). The consistency of visual sewer inspection data. Structure and Infrastructure Engineering, 9(3): 214–228CrossRefGoogle Scholar
  4. Gan L L, Zuo J E, Wang Y J, Low T S, Wang K J (2014). Comprehensive health condition assessment on partial sewers in a southern Chinese city based on fuzzy mathematic methods. Frontiers of Environmental Science & Engineering, 8(1): 144–150CrossRefGoogle Scholar
  5. Guo W, Soibelman L, Garrett J H Jr (2009). Automated defect detection for sewer pipeline inspection and condition assessment. Automation in Construction, 18(5): 587–596CrossRefGoogle Scholar
  6. Halfway M R, Hengmeechai J (2014). Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine. Automation in Construction, 38: 1–13CrossRefGoogle Scholar
  7. Halfawy M R, Hengmeechai J (2015). Integrated vision-based system for automated defect detection in sewer closed circuit television inspection videos. Journal of Computing in Civil Engineering, 29(1): 04014024CrossRefGoogle Scholar
  8. Hawari A, Alamin M, Alkadour F, Elmasry M, Zayed T (2018). Automated defect detection tool for closed circuit television (CCTV) inspected sewer pipelines. Automation in Construction, 89: 99–109CrossRefGoogle Scholar
  9. Hu M (1962). Visual-pattern recognition by moment invariants. I.R.E. Transactions on Information Theory, 8(2): 179–187CrossRefGoogle Scholar
  10. Iyer S, Sinha S K (2005). A robust approach for automatic detection and segmentation of cracks in underground pipeline images. Image and Vision Computing, 23(10): 921–933CrossRefGoogle Scholar
  11. Kumar S S, Abraham D M, Jahanshahi M R, Iseley T, Starr J (2018). Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks. Automation in Construction, 91: 273–283CrossRefGoogle Scholar
  12. Liang X (2006). Support Vector Machine-Based Image Classification Research. Dissertation for the Master Degree. Shanghai: Tongji University (in Chinese)Google Scholar
  13. Mashford J, Davis P, Rahilly M (2007). Pixel-based color image segmentation using support vector machine for automatic pipe inspection. In: M A Orgun, J Thornton, eds., Proc. of the 20th Australian Joint Conference on Artificial Intelligence. Heidelberg: Springer-Verlag,: 739–743Google Scholar
  14. Mashford J, Rahilly M, Davis P, Burn S (2010). A morphological approach to pipe image interpretation based on segmentation by support vector machine. Automation in Construction, 19(7): 875–883CrossRefGoogle Scholar
  15. National Bureau of Statistics of China. China Statistical Yearbook 1998–2013. Beijing: China Statistic Press, 1998–2013 (in Chinese)Google Scholar
  16. Panasiuk O, Hedström A, Marsalek J, Ashley R M, Viklander M (2015). Contamination of stormwater by wastewater: A review of detection methods. Journal of Environmental Management, 152: 241–250CrossRefGoogle Scholar
  17. Shehab T, Moselhi O (2005). Automated detection and classification of infiltration in sewer pipes. Journal of Infrastructure Systems, 11(3): 165–171CrossRefGoogle Scholar
  18. Su T C, Yang M D (2014). Application of morphological segmentation to leaking defect detection in sewer pipelines. Sensors (Basel), 14(5): 8686–8704CrossRefGoogle Scholar
  19. Tran Q A, Zhang Q L, Li X (2003). Reduce the number of support vectors by using clustering techniques. In: Proceedings of the 2nd international conference on machine learning and cybernetics, Xi’an, China, 1245–1248Google Scholar
  20. Yang M D, Su T C (2008). Automated diagnosis of sewer pipe defects based on machine learning approaches. Expert Systems with Applications, 35(3): 1327–1337CrossRefGoogle Scholar
  21. Yang M D, Yang Y F, Su T C, Huang K S (2014). An efficient fitness function in genetic algorithm classifier for landuse recognition on satellite images. The Scientific World JCrossRefGoogle Scholar
  22. Zhang J (2013). Research and Application of Diagnosis Technologies for Crop Pests based on Image Recognition. Dissertation for the Doctoral Degree. Hefei: University of Science and Technology of China (in Chinese)Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xiangyang Ye
    • 1
  • Jian’e Zuo
    • 1
    Email author
  • Ruohan Li
    • 1
    • 2
  • Yajiao Wang
    • 1
  • Lili Gan
    • 1
    • 3
  • Zhonghan Yu
    • 1
  • Xiaoqing Hu
    • 1
    • 2
  1. 1.State Key Joint Laboratory of Environment Simulation and Pollution Control, School of EnvironmentTsinghua UniversityBeijingChina
  2. 2.Thunip Corp., Ltd.BeijingChina
  3. 3.China Water Environment GroupBeijingChina

Personalised recommendations