0
  • DE
  • EN
  • FR
  • Internationale Datenbank und Galerie für Ingenieurbauwerke

Anzeige

Learning Structure for Concrete Crack Detection Using Robust Super-Resolution with Generative Adversarial Network

Autor(en): ORCID
ORCID
ORCID
ORCID
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Structural Control and Health Monitoring, , v. 2023
Seite(n): 1-16
DOI: 10.1155/2023/8850290
Abstrakt:

To develop countermeasures against the inevitable aging of tunnels, accurate inspection is critical for ensuring stable tunnel services. Tunnels are large-scale infrastructures, whereas the cracks in such tunnels are small-scale objects, necessitating the development of methods capable of rapidly detecting cracks in a wide field of view. Therefore, this study proposes a new learning structure based on a segmentation network. This structure includes super-resolution and generative adversarial networks, which contribute to improved detection performance with robustness to input data. Furthermore, a method for the effective construction of training data for the application of the proposed structure is presented. Subsequently, the performance of the method over 1,606 crack images with randomly degraded qualities is evaluated. The proposed structure presents improved crack intersection over union and F1-scores of 63.686% and 77.811%, respectively, and low variances of 0.9008 and 0.5015 compared to the original structure. The results presented herein indicate the possible application of the proposed accurate condition inspection technology to tunnel maintenance in the future.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1155/2023/8850290.
  • Über diese
    Datenseite
  • Reference-ID
    10725420
  • Veröffentlicht am:
    30.05.2023
  • Geändert am:
    30.05.2023
 
Structurae kooperiert mit
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine