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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    <subfield code="a">Defect detection</subfield>
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    <subfield code="a">Vessel inspection</subfield>
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    <subfield code="a">Corrosion</subfield>
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    <subfield code="a">Cracks</subfield>
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    <subfield code="a">Saliency</subfield>
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    <subfield code="a">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).</subfield>
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    <subfield code="a">Alberto Ortiz</subfield>
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    <subfield code="u">University of the Balearic Islands</subfield>
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    <subfield code="a">Francisco Bonnin-Pascual</subfield>
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    <subfield code="a">A novel approach for defect detection on vessel structures using saliency-related features</subfield>
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    <subfield code="a">Robotics Technology for Inspection of Ships</subfield>
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    <subfield code="c">605200</subfield>
    <subfield code="a">Inspection Capabilities for Enhanced Ship Safety</subfield>
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    <subfield code="a">&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;</subfield>
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