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Visual Inspection of Vessels by Means of a Micro-Aerial Vehicle: An Artificial Neural Network Approach for Corrosion Detection

  • Alberto Ortiz
  • Francisco Bonnin-Pascual
  • Emilio Garcia-Fidalgo
  • Joan P. Company
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 418)

Abstract

Periodic visual inspection of the different surfaces of a vessel hull is typically performed by trained surveyors at great cost, both in time and in economical terms. Assisting them during the inspection process by means of mechanisms capable of automatic or semi-automatic defect detection would certainly decrease the inspection cost. This paper describes a defect detection approach comprising: (1) a Micro-Aerial Vehicle (MAV) which is used to collect images from the surfaces under inspection, particularly focusing on remote areas where the surveyor has no visual access; and (2) a coating breakdown/corrosion detector based on a 3-layer feed-forward artificial neural network. The success of the classification process depends not only on the defect detector but also on a number of assistance functions that are provided by the control architecture of the aerial platform, whose aim is to improve picture quality. Both aspects are described along the different sections of the paper, as well as the classification performance attained.

Keywords

Corrosion detection MAV Artificial neural network 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alberto Ortiz
    • 1
  • Francisco Bonnin-Pascual
    • 1
  • Emilio Garcia-Fidalgo
    • 1
  • Joan P. Company
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of Balearic IslandsPalmaSpain

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