Estimation of pavement irregularities using digital image processing techniques.
Authors
- Luis David Flórez-Pareja Universidad de Antioquia
-
Juan Pablo Escobar-Arenas
Universidad de Antioquia
https://orcid.org/0000-0002-3255-5188
-
David Stephen Fernandez Mc Cann
Universidad de Antioquia
https://orcid.org/0000-0003-4898-4144
DOI:
https://doi.org/10.33571/rpolitec.v19n37a2Keywords:
Digital image processing, Pavement irregularities, Intersection over junction, Pavement evaluationAbstract
Transport routes play a fundamental role in the communication of a country, they are of vital importance both in the urban area and in the rural area, some of them are paved and constituted by various materials that with the passage of time can present different types of deterioration or irregularities. Therefore, with the following document it is intended to give a solution to estimate the damage caused by these irregularities that in sum are of interest to study the potholes, cracks and crocodile skins that are common with the passage of time in the respective road. With this objective, a treatment is carried out from the perspective of digital image processing using perspective transformation techniques, thresholds, filters, among others, in order to estimate the affected equivalent area that varies according to the irregularity, evaluating the veracity of the result by means of the intersection on union method (IOU) obtaining accuracy or precision values of 0.69, 0.87, 0.79 for deterioration such as crocodile skin, bumps and cracks conceived in this document.
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References
S. D. Indicators, “Road Infrastructure and Economic Development,” 1992.
R. Huincalef, G. Urrutia, G. Ingravallo, and D. C. Martínez, “Recognition of Surface Irregularities on Roads: a machine learning approach on 3D models,” 2018.
L. Tello-Cifuentes, M. Aguirre-Sánchez, J. P. Díaz-Paz, and F. Hernández, “Evaluación de daños en pavimento flexible usando fotogrametría terrestre y redes neuronales,” TecnoLógicas, vol. 24, no. 50, Jan. 2021, doi: 10.22430/22565337.1686.
D. Buchinger and A. G. Silva, “Anomalies detection in asphalt pavements: a morphological image processing approach,” Revista Brasileira de Computação Aplicada, vol. 6, no. 1, Apr. 2014, doi: 10.5335/rbca.2014.3661.
C. Koch and I. Brilakis, “Pothole detection in asphalt pavement images,” Advanced Engineering Informatics, vol. 25, no. 3, Aug. 2011, doi: 10.1016/j.aei.2011.01.002.
A. Cubero-Fernandez, Fco. J. Rodriguez-Lozano, R. Villatoro, J. Olivares, and J. M. Palomares, “Efficient pavement crack detection and classification,” EURASIP Journal on Image and Video Processing, vol. 2017, no. 1, Dec. 2017, doi: 10.1186/s13640-017-0187-0.
U. Escalona, F. Arce, E. Zamora, and J. H. Sossa Azuela, “Fully Convolutional Networks for Automatic Pavement Crack Segmentation,” Computación y Sistemas, vol. 23, no. 2, Jun. 2019, doi: 10.13053/cys-23-2-3047.
W. A. Mustafa, H. Aziz, W. Khairunizam, Z. Ibrahim, A. B. Shahriman, and Z. M. Razlan, “Review of Different Binarization Approaches on Degraded Document Images,” in 2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA 2018, 2018.
A. Vyas, S. Yu, and J. Paik, “Fundamentals of digital image processing,” in Signals and Communication Technology, 2018.
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Copyright (c) 2023 Luis David Flórez-Pareja, Juan Pablo Escobar-Arenas, David Stephen Fernandez Mc Cann
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
