Fully Convolutional Networks for Automatic Pavement Crack Segmentation

Authors

  • Uriel Escalona Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Fernando Arce Instituto Politécnico Nacional Centro de Investigación en Computación
  • Erik Zamora Instituto Politécnico Nacional Centro de Investigación en Computación
  • Juan Humberto Sossa Azuela Tecnológico de Monterrey, Campus Guadalajara, Jalisco

DOI:

https://doi.org/10.13053/cys-23-2-3047

Keywords:

Automatic pavement crack detection, pavement cracks, fully convolutional neural networks

Abstract

Pavement cracks are an increasing threat to public safety. Automatic pavement crack segmentation remains a very challenging problem due to crack texture inhomogeneity, high outlier potential, large variability of topologies, and so on. Due to this, automatic pavement crack detection has captured the attention of the computer vision community, and a great quantity of algorithms for solving this task have been proposed. In this work, we study a U-Net network and two variants for automatic pavement crack detection. The main contributions of this research are: 1) two U-Net based network variations for automatic pavement crack detection, 2) a series of experiments to demonstrate that the proposed architectures outperform the state-of-the-art for automatic pavement crack detection using two public and well-known challenging datasets: CFD and AigleRN and 3) the code for this approach.

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Published

2019-06-27

Issue

Vol. 23 No. 2 (2019): Topic Trends in Computing Research (Guest Editors: A. Aguilar Meléndez, E. U. Moya Sánchez)

Section

Articles

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