Author : N Surya 1
Date of Publication :1st August 2022
Abstract: Studies in the past have used image processing techniques on quality crack images obtained from open-source databases. The results obtained by using these techniques on such images yielded convincing results. But in an ideal situation, the environment in which the image is captured is often complex and dynamic, which does not guarantee good quality crack images. This study aims to compare the performance of four different image processing techniques used to detect concrete cracks when any crack image taken from a standard camera is used. The image processing techniques that have been considered are namely Wavelet transform, Min-Max Gray Level Discrimination, Super Pixel Segmentation and Nine Element Matrix method. The Otsu segmentation method is used to binarize the image after the images are preprocessed using these techniques, followed by the use of morphological operations to remove noisy spots. It was observed that Min-Max Gray Level Discrimination performed relatively better than the other techniques.
Reference :
-
- GHoang N-D (2018) Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding. Advances in Civil Engineering 2018:10. doi:10.1155/2018/3924120
- Chaki N, Shaikh SH, Saeed K (2014) Applications of Binarization. In: Exploring Image Binarization 218 Techniques. Springer India, New Delhi, pp 65-70. doi: 10. 1007/978-81-322-1907-15
- Yun Wang et al., “Research on Crack Detection Algorithm of the Concrete bridge based on Image Processing” , 8th International Congress of Information and Communication Technology, ICICT 2019
- Xuhang Tong et al., “A New Image-Based Method for Concrete Bridge Bottom Crack Detection”.
- Jonathan P. Rivera et al., “Automated Detection and Measurement of Cracks in Reinforced Concrete Components”, ACI Structural Journal, V. 112, No. 3, May-June 2015, MS No. S-2014-084.R1, doi: 10.14359 /51687424.
- Bang Yeon Lee, Yun Yong Kim, Seong-Tae Yi Jin-Keun Kim (2013) Automated image processing technique for detecting and analyzing concrete surface cracks, Structure and Infrastructure Engineering, 9:6, 567-577, DOI: 10.1080/ 15732479.2011.593891
- Shuncong Zhong and S Olutunde Oyadiji, “Crack Detection In Simply Supported Beams Using Stationary Wavelet Transform Of Modal Data”.
- Ikhlas Abdel-Qader et al., “Analysis of Edge-Detection Techniques for Crack Identification in Bridges”, https://doi.org/10.1061/(ASCE)0887- 3801(2003)17:4(255)
- N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetic, vol. 9, no. 1, pp. 62–66, 1979.
- Digital Image Processing For The Study Of Concrete Beam Cracks by Anis Salaheddin Sury, B.E.Sc (1999)
- Catherina Vasanthalin Prabakar and Chella Kavitha Nagarajan, “A novel approach of surface crack detection using super pixel segmentation”, https://doi.org/10.1016/ j.matpr.2020.12.114
- C¸.F. Ozgenel, Concrete crack images for classification, Retrieved from, https://doi. org/10.17632/5y9wdsg2zt.2, 2019.
- Shahid Kabir and Patrice Rivard, Damage classification of concrete structures based on gray level co-occurrence matrix using Haar’s wavelet transform, Computers and Concrete, Vol. 4, No. 3 (2007) 243-257