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A real-time deep learning network for ship detection in SAR images

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Abstract

In the field of ship detection in synthetic aperture radar (SAR) images using deep learning, traditional models often face challenges related to their complex structure and high computational demands. To address these problems, this paper proposes a lightweight end-to-end convolutional neural network called BGD-Net, which is based on anchor-free methods. This network incorporates a novel feature pyramid called Ghost, Cross-Stage-Partial, and Path Aggregation Network (G-CSP-PAN) to enhance the detection performance across targets of varying scales. Additionally, it introduces an efficient decoupled detection head, termed the efficient decoupled head (ED-Head), to enhance the interaction between regression and classification. Furthermore, an optimized loss function named optimized efficient intersection over union (OEIoU) loss is proposed for edge regression. The proposed method is evaluated on public datasets, SSDD and SAR-Ship-Dataset, demonstrating a balance between detection accuracy and efficiency.

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The datasets used or analyzed in the present study are accessible on the Internet.

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Funding

No funding was received for conducting this study.

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Authors and Affiliations

  1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Wenxue Zhou & Huachun Zhang

  2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China

    Wenxue Zhou & Huachun Zhang

Authors
  1. Wenxue Zhou
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  2. Huachun Zhang
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Contributions

W. X. Zhou was involved in conceptualization, investigation, software, validation, writing—original draft, and visualization. W. X. Zhou and H. C. Zhang were involved in methodology. H. C. Zhang was involved in resources, supervision, and writing—review and editing.

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Correspondence to Huachun Zhang.

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Zhou, W., Zhang, H. A real-time deep learning network for ship detection in SAR images. SIViP 18, 1893–1899 (2024). https://doi.org/10.1007/s11760-023-02892-6

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  • DOI: https://doi.org/10.1007/s11760-023-02892-6

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