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Automatic Classification Using Machine Learning for Non-Conventional Vessels on Inland Waters

1
Institute of Geoinformatics, Department of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
2
Marine Technology Ltd., ul. Roszczynialskiego 4/6, 81-521 Gdynia, Poland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3051; https://doi.org/10.3390/s19143051
Received: 23 May 2019 / Revised: 2 July 2019 / Accepted: 8 July 2019 / Published: 10 July 2019
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Abstract

The prevalent methods for monitoring ships are based on automatic identification and radar systems. This applies mainly to large vessels. Additional sensors that are used include video cameras with different resolutions. Such systems feature cameras that capture images and software that analyze the selected video frames. The analysis involves the detection of a ship and the extraction of features to identify it. This article proposes a technique to detect and categorize ships through image processing methods that use convolutional neural networks. Tests to verify the proposed method were carried out on a database containing 200 images of four classes of ships. The advantages and disadvantages of implementing the proposed method are also discussed in light of the results. The system is designed to use multiple existing video streams to identify passing ships on inland waters, especially non-conventional vessels. View Full-Text
Keywords: machine learning; image analysis; feature extraction; ship classification; marine systems machine learning; image analysis; feature extraction; ship classification; marine systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wlodarczyk-Sielicka, M.; Polap, D. Automatic Classification Using Machine Learning for Non-Conventional Vessels on Inland Waters. Sensors 2019, 19, 3051.

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