Automatic Road Crack Segmentation Using Thresholding Methods

Fauziah Kasmin, Zuraini Othman, Sharifah Sakinah Syed Ahmad

Abstract


Maintenance of good condition of roads are very essential to the economy and everyday life of people in a every country. Road cracks are one of the important indicators that show degradations of road surfaces. Inspection of roads that have been done manually took a very long time and tedious. Hence, an automatic road crack segmentation using thresholding methods have been proposed in this study. In this study, ten road crack images have been pre-processed as an initial step. Then, normalization techniques, L1-Sqrt norm have been applied onto images to reduce the variation of intensities that skewed to the right. Then, thresholding methods, Otsu and Sauvola methods have been used to binarize the images.  From the experiment of ten road crack images that have been done, normalization technique, L1-Sqrt norm can help to increase performance of road crack segmentation for Otsu and Sauvola methods. The results also show that Sauvola method outperform Otsu method in detecting road cracks.


Full Text:

PDF

References


H. Oliveira and P. L. Correia, “Automatic road crack segmentation using entropy and image dynamic thresholding,” Eur. Signal Process. Conf., no. Eusipco, pp. 622–626, 2009.

S. Riyadi, D. Rachmawati, and T. K. Hariadi, “Automatic Local Segmentation Technique for Detection of Road Surface Crack,” Int. J. Adv. Electron. Comput. Sci., vol. 3, no. 2, pp. 91–94, 2016.

G. Ting, W. Liu, Y. Yang, and W. Weixing, “Pavement Crack Image Segmentation Method based on Multiple Scale and Differential Box Dimension,” Int. J. Signal Process. Image Process. Pattern Recognit., vol. 10, no. 2, pp. 91–100, 2017.

Y. Shi, L. Cui, Z. Qi, F. Meng, and Z. Chen, “Automatic road crack detection using random structured forests,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 12, pp. 3434–3445, 2016.

Q. Zou, Y. Cao, Q. Li, Q. Mao, and S. Wang, “CrackTree: Automatic crack detection from pavement images,” Pattern Recognit. Lett., vol. 33, no. 3, pp. 227–238, 2012.

H. Oliveira and P. L. Correia, “Road surface crack Detection: Improved segmentation with pixel-based refinement,” 25th Eur. Signal Process. Conf. EUSIPCO 2017, vol. 2017–Janua, pp. 2026–2030, 2017.

Q. Li and X. Liu, “Novel approach to pavement image segmentation based on neighboring difference histogram method,” Proc. - 1st Int. Congr. Image Signal Process. CISP 2008, vol. 2, pp. 792–796, 2008.

Z. Sun, C. Wang, and A. Sha, “Study of image-based pavement cracking measurement techniques,” ICEMI 2009 - Proc. 9th Int. Conf. Electron. Meas. Instruments, pp. 2140–2143, 2009.

R. Medina, J. Llamas, J. Gómez-García-Bermejo, E. Zalama, and M. Segarra, “Crack detection in concrete tunnels using a gabor filter invariant to rotation,” Sensors (Switzerland), vol. 17, no. 7, pp. 1–16, 2017.

W. Zhang, Z. Zhang, D. Qi, and Y. Liu, “Automatic crack detection and classification method for subway tunnel safety monitoring,” Sensors, vol. 14, no. 10, pp. 19307–19328, 2014.

N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man, Cybern., vol. SMC-9, no. 1, pp. 62–66, 1979.

J. Sauvola and M. Pietikainen, “Adaptive document image binarization,” Pattern Recognit., vol. 33, pp. 225–236, 2000.

F. Kasmin, A. Abdullah, and A. Satria Prabuwono, “Ensembles of Normalization Techniques to Improve the Accuracy of Otsu Method,” Appl. Math. Sci., vol. 9, no. 32, pp. 1565–1578, 2015.

O. Nina, B. Morse, and W. Barrett, “A Recursive Otsu Thresholding Method for Scanned Document Binarization,” in IEEE Conference Proceedings, 2010, pp. 307–314.

W. A. Mustafa, H. Yazid, and M. Jaafar, “An Improved Sauvola Approach on Document Images Binarization,” J. Telecommun., vol. 10, no. 2, pp. 43–50, 2018.


Refbacks

  • There are currently no refbacks.


ISSN : 2590-3551, eISSN : 2600-8122     

Best viewed using Mozilla Firefox, Google Chrome and Internet Explorer with the resolution of 1280 x 800