MATHEMATICAL MODEL FOR THE PROBLEM  OF CLASSIFICATION OF PAVEMENT DEFECTS

Phạm Thị Liên; Trần Thị Tuyết; Nguyễn Quang Hiệp; Nguyễn Thị Dung; Kiều Tuấn Dũng; Nguyễn Thị Phương Dung

doi:10.34238/tnu-jst.5722

MATHEMATICAL MODEL FOR THE PROBLEM OF CLASSIFICATION OF PAVEMENT DEFECTS

About this article

Received: 19/03/22 Revised: 24/06/22 Published: 04/07/22

Authors

1. Pham Thi Lien , TNU - University of Information and Communication Technology
2. Tran Thi Tuyet, TNU - University of Information and Communication Technology
3. Nguyen Quang Hiep, TNU - University of Information and Communication Technology
4. Nguyen Thi Dung, TNU - University of Information and Communication Technology
5. Kieu Tuan Dung, Thuy Loi University
6. Nguyen Thi Phuong Dung, Thuy Loi University

Abstract

The road surface defect detection and classification system based on machine learning algorithms is already very advanced and is increasingly proving its outstanding advantages. In this paper, some image segmentation algorithms used in practice are presented, compared and evaluated. In this study, we present the convolution neural network—VGG16 structure to classify pavement defects, with a graph-based method to optimize the image segmentation on the pavement defect image. This proposed method is intended to overcome limitations caused by objective factors, such as high sensitivity to data of certain types of light and noise dependence, such as defect data of the road surface. Three different datasets were collected from the Center for Telecommunication and Multimedia, INESC TEC - Portugal (1200 images), Irkutsk city - Russian Federation (800 images) and Thai Nguyen city - Vietnam (550 images). The classification results based on the VGG-16 methods of the datasets in turn are good because the curves have a state closer to 1 than 0.5.

Keywords

Machine learning; Deep learning; Pavement defect classification; VGG16; Features extraction; Convolutional Neural Network

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References

[1] S. Chambon, “Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: Application on road crack detection,” Proceedings Image Processing: Machine Vision Applications II, Proc. SPIE, vol. 7251, 2009, doi: 10.1117/12.805437.

[2] C. Ma, C. Zaho, and Y. Hou, “Pavement distress detection based on nonsubsampled contourlet transform,” Proc. IEEE Int. CSSE, 2008, pp. 28-31.

[3] T. Nguyen, M. Avila, and B. Stephane, “Automatic detection and classification of defects on road pavement using anisotropy measure,” Proc.17th EUSIPCO, 2009, pp. 617-621.

[4] H. D. Cheng, “Novel approach to pavement cracking detection based on fuzzy set theory,” Journal of Computing in Civil Engineering, vol. 13, no. 4, pp. 270–280, 1999.

[5] F. Roli, “Measure of texture anisotropy for crack detection on textured surfaces,” Electronics Letters, vol. 32, pp. 1274–1279, 1996.

[6] J. Zhou, P. S. Huang, and F. P. Chiang, “Wavelet-based pavement distress detection and evaluation,” Optical Engineering, vol. 45, 2006, doi: 10.1117/1.2172917.

[7] O. Yashon and M. Hahn, “Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform,” Advanced Engineering Informatics, vol. 30, pp. 481–499, 2016.

[8] A. Downey, H. Koutsopoulos, and I. El Sanhouri, “Analysis of Segmentation Algorithms for Pavement Distress Images,” Journal of Transportation Engineering, vol. 119, pp. 869 – 888, 1993.

[9] Y. Hu and C. Zhao, “A local binary pattern based method for pavement crack detection,” Journal of Pattern Recognition Research, vol. 1, pp. 140–147, 2013.

[10] M. S. Kaseko and S. G. Ritchie, “A neural network-based methodology for pavement crack detection and classification,” Transportation Research Part C: Emerging Technologies, vol. 1, no. 4, pp. 275–291, 1993.

[11] K. R. Kirschke and S. A. Velinsky, “Histogram based approach for automated pavement crack sensing,” Journal of Transportation Engineering, vol. 118, no. 5, pp. 700–710, 1992.

[12] J. Zhou, P. S. Huang, and F. P. Chiang, “Wavelet-aided pavement distress image processing,” Optical Science and Technology, SPIE’s 48th Annual Meeting, 2003, pp. 728-739.

[13] F. Klugl, A. Bazzan, and S. Ossowski, “Agents in traffic and transportation,” Transp. Res. Part C: Emerg. Technol., vol. 18, pp. 69-70, 2010, doi: 10.1016/j.trc.2009.08.002.

[14] E. Dogan and A. Akgngr, “Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks,” Neural Comput. Appl., vol. 22, pp. 869-877, 2013.

[15] S. Budalakoti, A. Srivastava, and M. Ote, “Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety,” IEEE Trans. Syst. Man Cybern. Part C: Appl., vol. 39, pp. 101-113, 2009.

[16] R. Wang, S. Fan, and D. Work, “Efficient multiple model particle filtering for joint traffic state estimation and incident detection,” Transp. Res. Part C: Emerg. Technol., vol. 71, pp. 521-537, 2016.

[17] Y. Lv, Y. Duan, W. Kang, and Z. Li, “Traffic flow prediction with big data: A deep learning approach,” IEEE Trans. Intell. Transp. Syst., vol. 16, pp. 865-873, 2014.

[18] K. Gopalakrishnan, S. Khaitan, and A. Choudhary, “Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection,” Constr. Build. Mater, vol. 157, pp. 322-330, 2017.

[19] D. Zhang, Q. Li, Y. Chen, and M. Cao, “An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection,” Image Vis. Comput., vol. 57, pp. 130-146, 2017.

[20] G. Wu, X. Sun, L. Zhou, and H. Zhang, “Research on morphological wavelet operator for crack detection of asphalt pavement,” Proceedings of the 2016 IEEE International Conference on Information and Automation, Ningbo, China, 2016, pp. 1573-1577.

[21] H. Oliveira, J. Caeiro, and P. Correia, “Accelerated unsupervised filtering for the smoothing of road pavement surface imagery,” I4 Proceedings of the 2014 22nd European Signal Processing Conference (EUSIPCO), Lisbon, Portugal, 2016, pp. 2456-2469.

[22] M. R. Schlotjes, M. Burrow, H. Evdorides, and T. Henning, “Using support vector machines to predict the probability of pavement failure,” Proc. Inst. Civ. Eng. Transp., vol. 168, pp. 212-222, 2015.

[23] T. Nguyen, T. L. Nguyen, and A. I. Greglea, “Machine learning algorithms application to road defects,” Intelligent Decision Technologies, vol. 12, pp. 59-66, 2018.

[24] Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, pp. 1124–1137, 2004.

[25] C. Koch and I. Brilakis, “Improving Pothole Recognition through Vision Tracking for Automated Pavement Assessment,” Conference or Workshop Item, 2011, pp. 1-8.

[26] F. M. Nejad and H. Zakeri, “An optimum feature extraction method based on wavelet-Radon transform and dynamic neural network for pavement distress classification,” Expert Syst. Appl., vol. 8, pp. 9442–9460, 2011.

[27] H. N. Koutsopoulos and A. B. Downey, “Primitive-based classification of pavement cracking images,” Journal of Transportation Engineering, vol. 119, pp. 402–418, 1993.

[28] L. Gang, H. Yu-yao, and Z. Yan, “Automatic Recognition Algorithm of Pavement Defect Image Based on OTSU and Maximizing Mutual Information,” Microelectronics Computer, vol. 7, pp. 241–247, 2009.

[29] Fugro, “Automatic Road Analyzer,” 2011. [Online]. Available: http://www. roadware.com/products/9000. [Accessed October 10, 2021]

[30] Dhdv, “WayLink Digital Highway Data Vehicle,” 2011. [Online]. Available: http: //www.waylink. com/DHDV.htm. [Accessed October 10, 2021]

[31] K A. Abaza, S. A. Ashur, and I. A. Al-Khatib, “Integrated Pavement Management System with a Markovian Prediction Model,” Journal of Transp. Eng., vol. 130, no. 1, pp. 24–33, 2004.

[32] Gie, “Technologies Laservision,”, 2011. [Online]. Available: http://www.gieinc.ca/ main_en.html. [Accessed October 10, 2021]

[33] Q. Li, M. Yao, and X. Yao, “A real-time 3D Scanning System for Pavement Distortion Inspection,” Measurement Science and Technology, vol. 21, pp. 1–9, 2010.

[34] A. Makhmalbaf, M. W. Park, and J. Yang, “2D Vision Tracking Methods Performance Comparison for 3D Tracking of Construction Resources,” Proc. of Construction Research Congress., 2010, pp. 459-469.

DOI: https://doi.org/10.34238/tnu-jst.5722

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