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Crack Detection and Classification in Moroccan Pavement Using Convolutional Neural Network

Autor(en):



Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Infrastructures, , n. 11, v. 7
Seite(n): 152
DOI: 10.3390/infrastructures7110152
Abstrakt:

Crack is a condition indicator of the pavement’s structure. Generally, crack detection is an essential task for effective diagnosis of the road network. Moreover, evaluation of road quality is necessary to ensure traffic security. Since 2011, a periodic survey of approximately 57,500 km of Moroccan roads has been performed using an inspection vehicle (SMAC) which is equipped with high resolution cameras and GPS/DGPS receivers. Until recently, the teams of the National Center for Road Studies and Research (CNER) analyzed road surface states by visualization of pavement surface image sequences captured by the Multifunctional Pavement Assessment System (SMAC) in order to detect defects in road surfaces and classify them according to their type. However, this method involves manual processing and is complex, time consuming and subjective. In this paper, we propose an automated methodology for crack detection and classification in Moroccan flexible pavements using Convolutional Neural Networks (CNN). Transfer learning is also applied by testing a pre-trained Visual Geometry Group 19 (VGG-19) model. For the dataset used in this paper, the results indicate that good crack detection and classification are achieved using both models.

Copyright: © 2022 the Authors. Licensee MDPI, Basel, Switzerland.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10722792
  • Veröffentlicht am:
    22.04.2023
  • Geändert am:
    10.05.2023
 
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