An Improved Image Pre-processing Method for Concrete Crack Detection

  • Harsh KapadiaEmail author
  • Ripal Patel
  • Yash Shah
  • J. B. Patel
  • P. V. Patel
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Structure health monitoring of concrete structures has gained more attention in the recent years due to advancement in the technology. Different methods like acoustic, ultrasonic and image processing based inspection methods have been deployed to carry out an assessment of concrete structure. In this paper, work has been carried out to monitor the health of laboratory scale concrete objects using vision-based inspection. The objective is to provide a modified image pre-processing algorithms for accurate concrete crack detection. Different image processing based algorithms reviewed from existing literature were implemented and tested to detect cracks on the surface of a 15 × 15 × 15 cm concrete cube. Due to random unevenness on the surface of concrete blocks, designing of an accurate and robust algorithm becomes difficult and challenging. Developed algorithm was applied to different images of concrete cubes. Receiver operating characteristics analysis and computation time analysis along with result images were discussed in the paper. In order to validate the applicability of developed algorithm, test results of crack detection on practical crack images are presented. Python was used to develop algorithm along with OpenCV library for image processing functions.


Crack detection Image processing Python OpenCV 


  1. 1.
    Cheng C-C, Cheng T-M, Chiang C-H (2008) Defect detection of concrete structures using both infrared thermography and elastic waves. Autom Concr, pp 87–92Google Scholar
  2. 2.
    Adhikari RS, Moselhi O, Bagchi A (2014) Image-based retrieval of concrete crack properties for bridge inspection. Autom Constr 39:180–194CrossRefGoogle Scholar
  3. 3.
    Koch Christian, Georgieva Kristina, Kasireddy Varun, Akinci Burcu, Fieguth Paul (2015) A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv Eng Inform 29(2):196–210CrossRefGoogle Scholar
  4. 4.
    Mohan A, Poobal S (2017) Crack detection using image processing: a critical review and analysis. Alexandria Eng JGoogle Scholar
  5. 5.
    Lee BY, Kim YY, Yi S-T, Kim J-K (2013) Automated image processing technique for detecting and analyzing concrete surface cracks. Struct Infrastruct Eng 9(6):567–577CrossRefGoogle Scholar
  6. 6.
    Xu H, Tian Y, Lin S, Wang S (2013) Research of image segmentation algorithm applied to concrete bridge cracks. In: International conference on information science and technology (ICIST). IEEE, pp 1637–1640Google Scholar
  7. 7.
    Talab AMA, Huang Z, Xi F, HaiMing L (2016) Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik-Int J Light Electron Opt 127(3):1030–1033CrossRefGoogle Scholar
  8. 8.
    Miyamoto A, Konno M-A, Bruhwiler E (2007) Automatic crack recognition system for concrete structures using image processing approach. Asian J Inf Technol Medwell J 6(5)Google Scholar
  9. 9.
    Choudhary GK, Dey S (2012) Crack detection in concrete surfaces using image processing, fuzzy logic and neural network. In: IEEE fifth international conference on advanced computational intelligence (ICACI). Nanjing, Jiangsu, ChinaGoogle Scholar
  10. 10.
    Wang P, Huang H (2010) Comparison analysis on present image-based crack detection methods in concrete structure. In: 3rd international congress on image and signal processing (CISP). IEEE, pp 2530–2533Google Scholar
  11. 11.
    Yamaguchi T, Nakamura S, Saegusa R, Hashimoto S (2008) Image based crack detection for real concrete surfaces. IEEJ Trans Electr Electron Eng 3(1):128–135CrossRefGoogle Scholar
  12. 12.
    Fujita Y, Mitani Y, Hamamoto Y (2006) A method for crack detection on concrete structure. In: 18th international conference on pattern recognition ICPR. IEEE, pp 901–904Google Scholar
  13. 13.
    Atsushi I, Aoki Y, Hashimoto S (2002) Accurate extraction and measurement of fine cracks from concrete block surface image. In: Proceedings of the 2002 28th annual conference of the ieee industrial electronics, pp 2202–2207Google Scholar
  14. 14.
    Sharma A, and Mehta N (2016) Structural health monitoring using image processing techniques-a review. Int J Mod Comput Sci 4(4):93–97Google Scholar
  15. 15.
    Tomasi C, Manduchi R. (1998) Bilateral filtering for gray and color images. In: Sixth international conference on computer vision, IEEE. pp 839–846Google Scholar
  16. 16.
    2015. Smoothing Images. December 18. Accessed October 20, 2017.
  17. 17.
    Niblack and Sauvola Thresholding. Accessed March 22, 2018.
  18. 18.
    Saxena LP (2017) “Niblack’s binarization method and its modifications to real-time applications: a review. Artif Intell Rev 1–33Google Scholar
  19. 19.
    DFK 72AUC02 USB2.0 CMOS color industrial camera. Accessed November 2, 2017.
  20. 20.
    2014. OpenCV-Python Tutorials. November 14. Accessed December 12, 2017.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Harsh Kapadia
    • 1
    Email author
  • Ripal Patel
    • 1
  • Yash Shah
    • 1
  • J. B. Patel
    • 1
  • P. V. Patel
    • 1
  1. 1.Instrumentation and Control Engineering Department, Institute of TechnologyNirma UniversityAhmedabadIndia

Personalised recommendations