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Coordinate Attention and Transformer Neck-Based Marine Organism Detection

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Sensor Systems and Software (S-Cube 2022)

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Abstract

Marine organism detection is crucial for the intelligent construction of open-sea farm. Suffering from low-contrast, color-deviation and detail-blurry underwater environment, a coordinate attention and transformer neck-based benthonic organism detection (CATNBOD) scheme has been devised. Main contributions are as follows: 1) The coordinate attention (CA) module is designed in the feature extraction network to obtain meaningful features, such that the small-scale benthonic organisms can be accurately detected. 2) To efficiently address the challenge derived from intra- and inter-class occlusions of benthonic organism, the rotation window-based swin transformer (ST) module is devised in the neck structure. Combining with CA and ST modules contributes to the proposed CATNBOD scheme. The effectiveness and superiority have been sufficiently demonstrated on publicly available UDD dataset.

This work is supported by the National Natural Science Foundation of China (Grant 52271306), Innovative Research Foundation of Ship General Performance (Grant 31422120), and the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University (Grant 2022YBPY004).

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Authors and Affiliations

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

    Xiangjun Kong & Tingkai Chen

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

    Ning Wang & Yanzheng Chen

Authors
  1. Xiangjun Kong
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  2. Ning Wang
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  3. Tingkai Chen
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  4. Yanzheng Chen
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Corresponding author

Correspondence to Ning Wang .

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Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Hamid Reza Karimi 

  2. Dalian Maritime University, Dalian, China

    Ning Wang

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Kong, X., Wang, N., Chen, T., Chen, Y. (2023). Coordinate Attention and Transformer Neck-Based Marine Organism Detection. 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_3

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  • DOI: https://doi.org/10.1007/978-3-031-34899-0_3

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