Abstract
This paper presents a novel intelligent system for the automatic visual inspection of vessels consisting of three processing levels: (a) data acquisition: images are collected using a magnetic climbing robot equipped with a low-cost monocular camera for hull inspection; (b) feature extraction: all the images are characterized by 12 features consisting of color moments in each channel of the HSV space; (c) classification: a novel tool, based on an ensemble of classifiers, is proposed to classify sub-images as rust or non-rust. This paper provides a helpful roadmap to guide future research on the detection of rusting of metals using image processing.





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Acknowledgements
This work has been funded by the EC FP7 SST.2008.5.2.1 Innovative Product Concepts project: Marine INspection rObotic Assistant System, Grant No. SCP8-GA- 2009-233715-MINOAS, and by the BANDIERA (Best Action for National Development of International Expert Researchers Activities) project RITMARE. The authors thank Giorgio Bruzzone, Edoardo Spirandelli and Mauro Giacopelli for their extraordinary contribution to the design, development and test of the MARC platform, and Arturo Argentieri for technical assistance. Special thanks to the MINOAS consortium and, in particular, to Albena Todorova and all the personnel of Dolphin shipyards who supported with their high professionalism and kindness the field validation of the system.
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Maglietta, R., Milella, A., Caccia, M. et al. A vision-based system for robotic inspection of marine vessels. SIViP 12, 471–478 (2018). https://doi.org/10.1007/s11760-017-1181-9
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DOI: https://doi.org/10.1007/s11760-017-1181-9