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International School on Neural Networks, Initiated by IIASS and EMFCSC

NN 2022: Networked Control Systems for Connected and Automated Vehicles pp 1393–1401Cite as

Automatic Recognition of Draft Marks on a Ship’s Board Using Deep Learning System

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 510)

Abstract

One of the basic concepts affecting the technical-operational characteristics of the vessel is its draft, which in the conventional sense means vertical distance from water level to the lowest point of the bottom of the vessel. The displacement of the vessel can be calculated from the observed draft. Currently there are a number of ways to determine the draft, among which visual observation of marks of the vessel board of the officer responsible for this is the basic of world practice. However, all existing methods have different restrictions on the installation and use of systems necessary for their implementations or give a significant error when measuring the draft. As the main instrument to fix this problem in this paper the use of artificial neural networks of deep learning is proposed. An algorithm for frame-by-frame video processing using a system of technical vision for detection of marks on the vessel board and detection of operating waterline is given. Solving these two challenges will give a conclusion about the ship’s draft based on the extracted visual information. On the basis of experimental data, the possibility of introducing a technical vision system in solving the considered problem is assessed.

Keywords

  • Deep learning
  • Measurement
  • Draft marks
  • Computer vision

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  • DOI: 10.1007/978-3-031-11051-1_143
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Author information

Authors and Affiliations

  1. St. Petersburg State Marine Technical University, 3, Locmanskaya Street, 190121, St. Petersburg, Russia

    Yaroslav Kirilenko & Ignat Epifantsev

Authors
  1. Yaroslav Kirilenko
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  2. Ignat Epifantsev
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Corresponding author

Correspondence to Ignat Epifantsev .

Editor information

Editors and Affiliations

  1. Rostovskogo Strelkovogo Polka Narodnogo, Rostov State Transport University, Rostov-on-Don, Russia

    Alexander Guda

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Kirilenko, Y., Epifantsev, I. (2023). Automatic Recognition of Draft Marks on a Ship’s Board Using Deep Learning System. In: Guda, A. (eds) Networked Control Systems for Connected and Automated Vehicles. NN 2022. Lecture Notes in Networks and Systems, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-031-11051-1_143

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