Skip to main content
main-content

Tipp

Weitere Artikel dieser Ausgabe durch Wischen aufrufen

28.01.2020 | Original Article

A self-adaptive exhaustive search optimization-based method for restoration of bridge defects images

Zeitschrift:
International Journal of Machine Learning and Cybernetics
Autoren:
Eslam Mohammed Abdelkader, Mohamed Marzouk, Tarek Zayed
Wichtige Hinweise

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Existing bridges are aging and deteriorating. Furthermore, large number of bridges exist in transportation networks meanwhile maintenance budgets are being squeezed. This state of affairs necessities the development of automatic bridge defects evaluation model using computer vision technologies to overcome the limitations of visual inspection. The digital images are prone to degradation by noises during the image acquisition phase. The absence of efficient bridge defects image restoration method results in inaccurate condition assessment models and unreliable bridge management systems. The present study introduces a self-adaptive two-tier method for detection of noises and restoration of bridge defects images. The first model adopts Elman neural network coupled with invasive weed optimization algorithm to identify the type of noise that corrupts images. In the second model, moth-flame optimization algorithm is utilized to design a hybrid image filtering protocol that involves an integration of spatial domain and frequency domain filters. The proposed detection model was assessed through comparisons with other machine learning models as per split validation and tenfold cross validation. It attained the highest classification accuracies, whereas the accuracy, sensitivity, specificity, precision, F-measure and Kappa coefficient are 95.28%, 95.24%, 98.07%, 95.25%, 95.34%. 95.43% and 0.935, respectively in the separate noise recognition module. The capabilities of the proposed restoration model were evaluated against some well-known good-performing optimization algorithms in addition to some conventional restoration models. Moth-flame optimization algorithm outperformed other restoration models, whereas peak signal to noise ratio, mean-squared error, normalized absolute error and image enhancement factor are 25.359, 176.319, 0.0585 and 7.182, respectively.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literatur
Über diesen Artikel