DOI: https://doi.org/10.14710/jtsiskom.2020.13828

Three combination value of extraction features on GLCM for detecting pothole and asphalt road

Faculty of Computer Science, Brawijaya University. Veteran Road, Malang 65145, Indonesia

Received: 20 Jul 2020; Revised: 13 Nov 2020; Accepted: 27 Nov 2020; Available online: 7 Dec 2020; Published: 31 Jan 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
The rate of vehicle accidents in various regions is still high accidents caused by many factors, such as driver negligence, vehicle damage, and road damage. However, transportation technology developed very rapidly, for example, a smart car. The smart car is land transportation that does not use humans as drivers but uses machines automatically. However, vehicle accidents are still possible because automatic machines do not have the intelligence like humans to see all the vehicle's obstacles. Obstacles can take many forms, one of them is road potholes. We propose a method for detecting road potholes using the Gray-Level Cooccurrence Matrix with three features and using the Support Vector Machine as a classification method. We analyze the combination of GLCM Contrast, Correlation, and Dissimilarity features. The results showed that the combination of Contrast and Dissimilarity features had the best accuracy of 92.033 %, with a computing time of 0.0704 seconds per frame.
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Keywords: pothole; detection; GLCM; SVM; transportation
Funding: Brawijaya University

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Article Info
Section: Original Research Articles
Language : EN
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  1. C. K. Y. Lam Loong Man, Y. Koonjul, and L. Nagowah, “A low cost autonomous unmanned ground vehicle,” Future Computing and Informatics Journal, vol. 3, no. 2, pp. 304–320, 2018. doi: 10.1016/j.fcij.2018.10.001
  2. Badan Pusat Statistik Indonesia, Statistik transportasi darat 2017 (land transportation statistics 2017). Badan Pusat Statistik Indonesia, pp. 1–74, 2017
  3. Z. Zeng, T. Hu, and X. An, “Fast nonparametric road disparity estimation and gradient constrained obstacle detection for ugv navigation,” Journal of Physics: Conference Series, vol. 1087, no. 6, 062010, 2018. doi: 10.1088/1742-6596/1087/6/062010
  4. H. Sawalakhe and R. Prakash, “Development of roads pothole detection system using image processing,” Intelligent Embedded System, vol. 492, pp. 187–195, 2018. doi: 10.1007/978-981-10-8575-8_20
  5. N.-D. Hoang, “An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and neural network with steerable filter-based feature extraction,” Advanced in Civil Engineering, vol. 2018, pp. 1–12, 2018. doi: 10.1155/2018/7419058
  6. C. Koch and I. Brilakis, “Pothole detection in asphalt pavement images,” Advanced Engineering Informatics, vol. 25, no. 3, pp. 507–515, 2011. doi: 10.1016/j.aei.2011.01.002
  7. P. Wang, Y. Hu, Y. Dai, and M. Tian, “Asphalt pavement pothole detection and segmentation based on wavelet energy field,” Mathematic Problems in Engineering, vol. 2017, 1604130, pp. 1–13, 2017. doi: 10.1155/2017/1604130
  8. I. Sutrisno et al., “Design of pothole detector using gray level co-occurrence matrix (glcm) and neural network (nn),” IOP Conference Series: Materials Science and Engineering, vol. 874, 012012, 2020. doi: 10.1088/1757-899X/874/1/012012
  9. A. Güneş, H. Kalkan, and E. Durmuş, “Optimizing the color-to-grayscale conversion for image classification,” Signal, Image, and Video Processing, vol. 10, no. 5, pp. 853–860, 2016. doi: 10.1007/s11760-015-0828-7
  10. A. Chaddad, P. O. Zinn, and R. R. Colen, “Radiomics texture feature extraction for characterizing GBM phenotypes using GLCM,” in 12th International Symposium on Biomedical Imaging, Brooklyn, USA, Apr. 2015, pp. 84–87. doi: 10.1109/ISBI.2015.7163822
  11. F. Mohanty, S. Rup, B. Dash, B. Majhi, and M. N. S. Swamy, “Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach,” Neural Computing and Applications, vol. 32, pp. 7029-7043, 2019, doi: 10.1007/s00521-019-04186-w
  12. M. A. Tahir, A. Bouridane, F. Kurugollu, and A. Amira, “Accelerating the computation of GLCM and haralick texture features on reconfigurable hardware,” in International Conference on Image Processing, Singapore, Oct. 2004, pp. 2857–2860. doi: 10.1109/ICIP.2004.1421708
  13. P. C. Vasanth and K. R. Nataraj, “Facial expression recognition using SVM classifier,” Indonesian Journal of Electrical Engineering and Informatics, vol. 3, no. 1, pp. 16–20, 2015. doi: 10.11591/ijeei.v3i1.126
  14. M. S. Tehrany, B. Pradhan, S. Mansor, and N. Ahmad, “Flood susceptibility assessment using GIS-based support vector machine model with different kernel types,” Catena, vol. 125, pp. 91–101, 2015. doi: 10.1016/j.catena.2014.10.017
  15. S. Marianingsih and F. Utaminingrum, “Comparison of support vector machine classifier and Naïve Bayes classifier on road surface type classification,” in 3rd International Conference Sustainable Information Engineering and Technology, Malang, Indonesia, Nov. 2018, pp. 48–53. doi: 10.1109/SIET.2018.8693113
  16. B. C. Kuo, H. H. Ho, C. H. Li, C. C. Hung, and J. S. Taur, “A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 1, pp. 317–326, 2014. doi: 10.1109/JSTARS.2013.2262926

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