Detección inteligente de baches y estimación de su profundidad con Aprendizaje Profundo para la conservación de carreteras Intelligent Pothole Detection and Depth Estimation Using Deep Learning for Road Conservation
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mar 19, 2025
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https://doi.org/10.30878/ces.v32n0a37
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Mario Alberto Roman Garay
http://orcid.org/0000-0002-4328-4651
Carlos Alberto Hernández Beltran
http://orcid.org/0000-0002-5317-2506
Luis Alberto Morales Rosales
http://orcid.org/0000-0002-4753-9375
Hector Rodriguez Rangel
http://orcid.org/0000-0003-4999-3472
Miguel Antonio Villa Camacho
http://orcid.org/0009-0003-2086-9619
Jorge Luis Soto Audelo
http://orcid.org/0009-0004-6704-6756
Peter Lepej
http://orcid.org/0000-0002-7693-1966
http://orcid.org/0000-0002-4328-4651
Carlos Alberto Hernández Beltran
http://orcid.org/0000-0002-5317-2506
Luis Alberto Morales Rosales
http://orcid.org/0000-0002-4753-9375
Hector Rodriguez Rangel
http://orcid.org/0000-0003-4999-3472
Miguel Antonio Villa Camacho
http://orcid.org/0009-0003-2086-9619
Jorge Luis Soto Audelo
http://orcid.org/0009-0004-6704-6756
Peter Lepej
http://orcid.org/0000-0002-7693-1966
Resumen
El deterioro de las carreteras, visible en forma de baches, conlleva altos costos de reparación y aumenta el riesgo de accidentes. Este estudio aborda la detección inteligente de baches y la estimación precisa de su profundidad utilizando técnicas de aprendizaje profundo para la conservación de carreteras en entornos urbanos. Para ello, se emplea una cámara de profundidad y la red convolucional YOLOv8, entrenada con 273 imágenes, obteniendo mAP de 84.7%, precisión de 85.7% y recall de 76.3%. Los resultados demuestran un error promedio de 5 mm en la estimación de profundidad. Este trabajo ofrece una herramienta que apoya la gestión de infraestructura vial al proporcionar un inventario de la profundidad de los baches, facilitando la planificación de tareas de mantenimiento.
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Como citar
ROMAN GARAY, Mario Alberto et al.
Detección inteligente de baches y estimación de su profundidad con Aprendizaje Profundo para la conservación de carreteras.
CIENCIA ergo-sum, [S.l.], v. 32, mar. 2025.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/22933>. Fecha de acceso: 27 abr. 2025
doi: https://doi.org/10.30878/ces.v32n0a37.
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Ciencias exactas y aplicadas
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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Ranganathan, D. G. (2021). A study to find facts behind preprocessing on deep learning algorithms. Journal of Innovative Image Processing, 3(1), 66-74.
De Blasiis, M. R., Di Benedetto, A., & Fiani, M. (2020). Mobile laser scanning data for the evaluation of pavement surface distress. Remote Sensing, 12(6), 942.
Bosurgi, G., Modica, M., Pellegrino, O., & Sollazzo, G. (2023). An automatic pothole detection algorithm using pavement 3D data. International Journal of Pavement Engineering, 24(2), 2057978.
Wu, H., Yao, L., Xu, Z., Li, Y., Ao, X., Chen, Q., ... & Meng, B. (2019). Road pothole extraction and safety evaluation by integration of point cloud and images derived from mobile mapping sensors. Advanced Engineering Informatics, 42, 100936.
Peralta-López, J. E., Morales-Viscaya, J. A., Lázaro-Mata, D., Villaseñor-Aguilar, M. J., Prado-Olivarez, J., Pérez-Pinal, F. J., ... & Barranco-Gutiérrez, A. I. (2023). Speed bump and pothole detection using deep neural network with images captured through zed camera. Applied Sciences, 13(14), 8349.
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Kang, B. H., & Choi, S. I. (2017, July). Pothole detection system using 2D LiDAR and camera. In 2017 ninth international conference on ubiquitous and future networks (ICUFN) (pp. 744-746). IEEE.
She, X., Hongwei, Z., Wang, Z., & Yan, J. (2021). Feasibility study of asphalt pavement pothole properties measurement using 3D line laser technology. International Journal of Transportation Science and Technology, 10(1), 83-92.