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Ensemble Transfer Learning Framework for Vessel Size Estimation from 2D Images

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

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Abstract

The term gross tonnage refers to the internal volume of a vessel and it has several legal, administrative and safety uses. Therefore, there is significant value in developing a mechanism for the automatic estimation of vessel size based on 2D images taken in uncontrolled conditions. However, this is a demanding task as vessels can be photographed from various angles and distances, a part of a vessel can be obstructed, or a vessel can blend with the background. We proposed an ensemble of fine-tuned transfer learning models, which we trained on 20,000 images in a training dataset consisting of randomly downloaded images from the Shipspotting website. Multiple deep learning methods were applied and modified for regression problems, together with two classical machine learning algorithms. A detailed analysis of model performances was given, based on which it can be concluded that such an approach results in a vessel size evaluation of the same quality as with the best human experts from the corresponding field.

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Authors and Affiliations

  1. University of Dubrovnik, Dubrovnik, Croatia

    Mario Miličević, Krunoslav Žubrinić, Ivan Grbavac & Ana Kešelj

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  1. Mario Miličević
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  2. Krunoslav Žubrinić
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  3. Ivan Grbavac
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  4. Ana Kešelj
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Corresponding author

Correspondence to Mario Miličević .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Miličević, M., Žubrinić, K., Grbavac, I., Kešelj, A. (2019). Ensemble Transfer Learning Framework for Vessel Size Estimation from 2D Images. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_22

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20517-1

  • Online ISBN: 978-3-030-20518-8

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