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.
This work is partially supported by the European Social Fund through grant FPI11-43123621R (Conselleria d’Educacio, Cultura i Universitats, Govern de les Illes Balears) and by the EU FP7 project INCASS (GA 605200). This publication reflects only the authors’ views and the European Union is not liable for any use that may be made of the information contained therein. The authors also thank Fabricio Ardizon for his involvement in project INCASS.
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Ortiz, A., Bonnin-Pascual, F., Garcia-Fidalgo, E., Company, J.P. (2016). Visual Inspection of Vessels by Means of a Micro-Aerial Vehicle: An Artificial Neural Network Approach for Corrosion Detection. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-319-27149-1_18
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