Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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 87–92
Adhikari RS, Moselhi O, Bagchi A (2014) Image-based retrieval of concrete crack properties for bridge inspection. Autom Constr 39:180–194
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):196–210
Mohan A, Poobal S (2017) Crack detection using image processing: a critical review and analysis. Alexandria Eng J
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):567–577
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 1637–1640
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):1030–1033
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)
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, China
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 2530–2533
Yamaguchi T, Nakamura S, Saegusa R, Hashimoto S (2008) Image based crack detection for real concrete surfaces. IEEJ Trans Electr Electron Eng 3(1):128–135
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 901–904
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 2202–2207
Sharma A, and Mehta N (2016) Structural health monitoring using image processing techniques-a review. Int J Mod Comput Sci 4(4):93–97
Tomasi C, Manduchi R. (1998) Bilateral filtering for gray and color images. In: Sixth international conference on computer vision, IEEE. pp 839–846
2015. Smoothing Images. December 18. Accessed October 20, 2017. https://docs.opencv.org/3.1.0/d4/d13/tutorial_py_filtering.html
Niblack and Sauvola Thresholding. Accessed March 22, 2018. http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_niblack_sauvola.html
Saxena LP (2017) “Niblack’s binarization method and its modifications to real-time applications: a review. Artif Intell Rev 1–33
DFK 72AUC02 USB2.0 CMOS color industrial camera. Accessed November 2, 2017. https://www.theimagingsource.com/products/industrial-cameras/usb-2.0-color/dfk72auc02/
2014. OpenCV-Python Tutorials. November 14. Accessed December 12, 2017. https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_tutorials.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kapadia, H., Patel, R., Shah, Y., Patel, J.B., Patel, P.V. (2019). An Improved Image Pre-processing Method for Concrete Crack Detection. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_149
Download citation
DOI: https://doi.org/10.1007/978-3-030-00665-5_149
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00664-8
Online ISBN: 978-3-030-00665-5
eBook Packages: EngineeringEngineering (R0)