Abstract
Roads are composed of various sorts of materials and with the constant use they expose different kinds of cracks or potholes. The aim of the current research is to present a novel automated classification method to be applied on these faults, which can be located on rigid pavement type. In order to collect proper representation of faults, a Kinect device was used, leading to three-dimensional point cloud structures. Images descriptors were used in order to establish the type of pothole and to get information regarding fault dimensions.
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References
Point cloud library (PCL). https://www.pointclouds.org
Scikit learn (support vector machines). http://scikit-learn.org/stable/modules/svm.html
Brink, H., Richards, J.W., Fetherolf, M.: Real-World Machine Learning. Manning Publications, Greenwich (2017)
Buza, E., Omanovic, S., Huseinovic, A.: Pothole detection with image processing and spectral clustering. In: Recent Advances in Computer Science and Networking, pp. 48–53. WSEAS (2013)
Casas-Avellaneda, D., López-Parra, J.: Detection and localization of potholes in roadways using smartphones. DYNA Mag. 83, 156–162 (2016)
Chen, K., Lu, M., Fan, X.: Road condition monitoring using on-board three-axis acelerometer. In: 6th International ICST Conference on Communications and Networking in China. IEEE (2011)
Eriksson, G., Hull, N., Madden, B.: The pothole patrol: using a mobile sensor network for road surface monitoring. In: MobiSys (2008)
Huincalef, R., Urrutia, G., Ingravallo, G., Martínez,D.: Recognition of surface irregularities on roads: a machine learning approach on 3D models. In: XXIV Congreso Argentino de Ciencias de la Computación, pp. 401–411. Red de Universidades con Carreras en Informática (RedUNCI) (2018). ISBN 978-950-658-472-6
Korch, B.: Pothole detection in asphalt pavement images. Adv. Eng. Inform. 25-3, 507–515 (2011). Elsevier
Mathavan, S., Rahman, M., Kamal, K., Usman, S., Moazzam, I.: Metrology and visualization of potholes using the Microsoft Kinect sensor. In: 16th International IEEE Annual Conference on Intelligent Transportation Systems (2013)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Nieber, S., Brooysen, M., Kroon, R.: Detection potholes using simple processing techniques and real-world footage. In: Proceedings of the 34th Southern African Transport Conference (2015)
Ryu, S.K., Kim, T., Kim, Y.R.: Image-based pothole detection system for its service and road management system. In: Mathematical Problems in Engineering, pp. 1–10 (2015)
Tecimer, A., Taysi, Z., Yavuz, A., Yavuz, M.K.: Assessment of vehicular transportation quality via smartphones. Turk. J. Electr. Eng. Comput. Sci. 23, 2161–2170 (2013)
Xie, S.: 3D pavement surface reconstruction and cracking recognition using Kinect-based solution. Technical report, University of New Mexico (2015)
Yamaguchi, T., Nakamura, S., Saegusa, R., Hashimoto, S.: Image-based crack detection for real concrete surfaces. IEEE Trans. Electr. Electron. Eng. 3, 128–135 (2008). Wiley Interscience
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Huincalef, R., Urrutia, G., Ingravallo, G., Martínez, D.C. (2019). An Approach to Automated Recognition of Pavement Deterioration Through Machine Learning. In: Pesado, P., Aciti, C. (eds) Computer Science – CACIC 2018. CACIC 2018. Communications in Computer and Information Science, vol 995. Springer, Cham. https://doi.org/10.1007/978-3-030-20787-8_9
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