Image Segmentation of Bricks in Masonry Wall Using a Fusion of Machine Learning Algorithms

Roland Kajatin, Lazaros Nalpantidis

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

Autonomous mortar raking requires a computer vision system which is able to provide accurate segmentation masks of close-range
images of brick walls. The goal is to detect and ultimately remove the mortar, leaving the bricks intact, thus automating this constructionrelated task. This paper proposes such a vision system based on the combination of machine learning algorithms. The proposed system fuses the individual segmentation outputs of eight classifiers by means of a weighted voting scheme and then performing a threshold operation to generate the final binary segmentation. A novel feature of this approach is the fusion of several segmentations using a low-cost commercial offthe-shelf hardware setup. The close-range brick wall segmentation capabilities of the system are demonstrated on a total of about 9 million data points.
Original languageEnglish
Title of host publicationProceedings of ICPR 2020 workshop on Pattern Recognition in Construction and the Built Environment
PublisherSpringer
Publication date2021
Pages446-461
ISBN (Print)9783030687861
DOIs
Publication statusPublished - 2021
EventICPR 2020 workshop on Pattern Recognition in Construction and the Built Environment - Virtual event, Milan, Italy
Duration: 10 Jan 202110 Jan 2021

Conference

ConferenceICPR 2020 workshop on Pattern Recognition in Construction and the Built Environment
LocationVirtual event
CountryItaly
CityMilan
Period10/01/202110/01/2021

Keywords

  • Image segmentation
  • Construction robotics
  • Machine learning
  • Deep learning

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