Journal article Open Access

A novel approach for defect detection on vessel structures using saliency-related features

Francisco Bonnin-Pascual; Alberto Ortiz


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  <identifier identifierType="DOI">10.5281/zenodo.4408152</identifier>
  <creators>
    <creator>
      <creatorName>Francisco Bonnin-Pascual</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8253-7455</nameIdentifier>
      <affiliation>University of the Balearic Islands</affiliation>
    </creator>
    <creator>
      <creatorName>Alberto Ortiz</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4215-3764</nameIdentifier>
      <affiliation>University of the Balearic Islands</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A novel approach for defect detection on vessel structures using saliency-related features</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Defect detection</subject>
    <subject>Vessel inspection</subject>
    <subject>Corrosion</subject>
    <subject>Cracks</subject>
    <subject>Saliency</subject>
    <subject>Micro-Aerial Vehicle</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-02-01</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4408152</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4408151</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Seagoing vessels have to undergo regular visual inspections in order to detect defects such as cracks and corrosion&amp;nbsp;before they result into catastrophic consequences. These inspections are currently performed manually by ship&amp;nbsp;surveyors at a great cost, so that any level of assistance during the inspection process by means of e.g. a fleet of&amp;nbsp;robots capable of defect detection would significatively decrease the inspection cost. In this paper, we describe a&amp;nbsp;novel framework for visually detecting the aforementioned defects. This framework is generic and flexible in the&amp;nbsp;sense that it can be easily configured to compute the features that perform better for the inspection at hand.&amp;nbsp;Making use of this framework and inspired by the idea of conspicuity, this work considers contrast and symmetry&amp;nbsp;as features for detecting defects and shows their usefulness for the case of vessels. Three different combination&amp;nbsp;operators are additionally tested in order to merge the information provided by these features and improve the detection performance. Experimental results for different configurations of the detection framework show better&amp;nbsp;classification rates than state of the art methods and prove its usability for images collected by a micro-aerial&amp;nbsp;robotic platform intended for visual inspection.&lt;/p&gt;</description>
    <description descriptionType="Other">This is a preprint version of publication with DOI: https://doi.org/10.1016/j.oceaneng.2017.08.024. This work is partially supported by FEDER funding, by project nr. AAEE50/2015 (Direccio General d'Innovacio i Recerca, Govern de les Illes Balears).</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/779776/">779776</awardNumber>
      <awardTitle>Robotics Technology for Inspection of Ships</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100011102</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/FP7/605200/">605200</awardNumber>
      <awardTitle>Inspection Capabilities for Enhanced Ship Safety</awardTitle>
    </fundingReference>
  </fundingReferences>
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