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Open AccessArticle

Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application

Department of Mathematics and Computer Science, University of Balearic Islands, Palma de Mallorca 07122, Spain
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Author to whom correspondence should be addressed.
Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López
Sensors 2016, 16(12), 2118; https://doi.org/10.3390/s16122118
Received: 2 October 2016 / Revised: 22 November 2016 / Accepted: 7 December 2016 / Published: 14 December 2016
(This article belongs to the Special Issue Vision-Based Sensors in Field Robotics)
Vessel maintenance requires periodic visual inspection of the hull in order to detect typical defective situations of steel structures such as, among others, coating breakdown and corrosion. These inspections are typically performed by well-trained surveyors at great cost because of the need for providing access means (e.g., scaffolding and/or cherry pickers) that allow the inspector to be at arm’s reach from the structure under inspection. This paper describes a defect detection approach comprising a micro-aerial vehicle which is used to collect images from the surfaces under inspection, particularly focusing on remote areas where the surveyor has no visual access, and a coating breakdown/corrosion detector based on a three-layer feed-forward artificial neural network. As it is discussed in the paper, the success of the inspection process depends not only on the defect detection software but also on a number of assistance functions provided by the control architecture of the aerial platform, whose aim is to improve picture quality. Both aspects of the work are described along the different sections of the paper, as well as the classification performance attained. View Full-Text
Keywords: vessel inspection; defect detection; unmanned aerial vehicle; supervised autonomy; machine learning; artificial neural network vessel inspection; defect detection; unmanned aerial vehicle; supervised autonomy; machine learning; artificial neural network
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Ortiz, A.; Bonnin-Pascual, F.; Garcia-Fidalgo, E.; Company-Corcoles, J.P. Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application. Sensors 2016, 16, 2118.

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