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Marine Vessel Detection in Sea Fog Environment Based on SSD

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Sensor Systems and Software (S-Cube 2022)

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Abstract

Aiming at solving the problem of low marine vessel detection accuracy in the sea fog environment, a deep learning-based anti-fog marine vessel detection method is proposed in this paper by combining defogging preprocessing with marine vessel detection model. Firstly, gated context aggregation network (GCANet) network is used to process the marine vessel image. Then, the processed image is sent to a modified SSD network, wherein anchors are tuned by statistical characteristics of the shape of marine vessel to detect the position of the marine vessel. Furthermore, to alleviate the loss of feature information due to defogging processing, channel attention mechanism based on the squeeze and excitation module (SE) is added to base convolutional layer of SSD. The comprehensive experiments and comparison results show that the proposed G-SEMSSD network is more suitable for marine vessel detection under sea fog environment.

This work is supported by the National Natural Science Foundation of China (Grant 52271306) and Innovative Research Foundation of Ship General Performance (Grant 31422120).

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Authors and Affiliations

  1. School of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China

    Yuanyuan Wang, Luyuan Tang & Wei Wu

  2. School of Marine Engineering, Dalian Maritime University, Dalian, 116026, China

    Ning Wang

Authors
  1. Yuanyuan Wang
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  2. Ning Wang
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  3. Luyuan Tang
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  4. Wei Wu
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Corresponding author

Correspondence to Ning Wang .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Hamid Reza Karimi 

  2. Dalian Maritime University, Dalian, China

    Ning Wang

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, Y., Wang, N., Tang, L., Wu, W. (2023). Marine Vessel Detection in Sea Fog Environment Based on SSD. In: Karimi , H.R., Wang, N. (eds) Sensor Systems and Software. S-Cube 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 487. Springer, Cham. https://doi.org/10.1007/978-3-031-34899-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-34899-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34898-3

  • Online ISBN: 978-3-031-34899-0

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