A multi-objective invasive weed optimization method for segmentation of distress images

Eslam Mohammed Abdelkader, Osama Moselhi, Mohamed Marzouk, Tarek Zayed

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


Image segmentation is one of the fundamental stages in computer vision applications. Several meta-heuristics have been applied to solve the segmentation problems by extending the Otsu and entropy functions. However, no single-objective function can optimally handle the diversity of information in images besides the multimodality issues of gray-level images. This paper presents a self-adaptive multi-objective optimization-based method for the detection of crack images in reinforced concrete bridges. The proposed method combines the flexibility of information theory functions in addition to the invasive weed optimization algorithm for bi-level thresholding. The capabilities of the proposed method are demonstrated through comparisons with singleobjective optimization-based methods, conventional segmentation methods, multi-objective genetic algorithm-based method, multi-objective particle swarmbased method and multi-objective harmony search-based method. The proposed method outperformed the previously-mentioned segmentation methods, whereas the average values of mean-squared error, peak signal to noise ratio and structural similarity index are equal to 0.0784, 11.4831 and 0.9921, respectively.

Original languageEnglish
Pages (from-to)643-661
Number of pages19
JournalIntelligent Automation and Soft Computing
Issue number4
Publication statusPublished - 2020


  • Crack
  • Image segmentation
  • Invasive weed optimization algorithm
  • Meta-heuristics
  • Multi-objective optimization
  • Multimodality
  • Reinforced concrete bridges

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence


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