Acta Optica Sinica, Volume. 45, Issue 5, 0528003(2025)

Lightweight Multi‑Scale Synthetic Aperture Radar Ship Detection Algorithm

Changzhen Xiong1... Xiyu Li1,*, Heyi Zhao2 and Songming Xie1 |Show fewer author(s)
Author Affiliations
  • 1School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
  • 2School of Mechanical and Materials Engineering, North China University of Technology, Beijing 100144, China
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    Figures & Tables(10)
    Network structure of proposed algorithm
    Structures of Conv, bottleneck, and C2f
    Structure of MSPPF
    Structure of EMA
    Structure of LSCH
    Comparisons of detection results between YOLOv8 and proposed algorithm on HRSID dataset
    • Table 1. Results of ablation experiments on HRSID and SSDD datasets

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      Table 1. Results of ablation experiments on HRSID and SSDD datasets

      DatasetLSCHMSPPFFocaler-ShapeIoUParams /MFLOPs /GP /%R /%AP_0.5 /%AP_0.5∶0.95 /%
      HRSID3.08.291.082.090.565.8
      2.26.190.982.391.066.6
      2.36.392.383.891.767.2
      2.36.392.584.092.067.6
      SSDD3.08.294.991.497.069.1
      2.26.194.891.998.270.1
      2.36.395.894.498.472.0
      2.36.396.194.198.672.2
    • Table 2. Results of contrast experiments on loss functions

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      Table 2. Results of contrast experiments on loss functions

      DatasetMethodFocalerAP_0.5 /%AP_0.5∶0.95 /%
      HRSIDCIoU×91.767.2
      91.967.4
      SIoU×91.667.1
      91.767.2
      ShapeIoU×91.867.3
      92.167.6
      SSDDCIoU×98.472.0
      98.572.1
      SIoU×98.372.0
      98.472.2
      ShapeIoU×98.572.1
      98.672.2
    • Table 3. Contrast experimental results on HRSID and SSDD datasets

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      Table 3. Contrast experimental results on HRSID and SSDD datasets

      DatasetAlgorithmParams /MFLOPs /GP /%R /%AP_0.5 /%AP_0.5∶0.95 /%
      HRSIDFaster R-CNN41.4134.482.490.588.0
      Libra R-CNN42.8141.383.689.788.8
      YOLOv3103.7283.090.384.091.066.3
      YOLOv8n3.08.291.082.090.565.8
      FCOS32.1126.062.986.184.5
      FENDet120.679.088.789.9
      GCBANet948.089.8
      FL-CSEvROIE107.690.2
      YOLO-CLF91.883.790.462.7
      MCMA-Net80.488.987.4
      Ours2.36.392.584.092.067.6
      SSDDFaster R-CNN41.4134.467.294.589.5
      Libra R-CNN42.8141.366.894.788.9
      YOLOv3103.7283.090.793.795.070.2
      YOLOv8n3.08.294.991.497.069.1
      FCOS32.1126.084.793.895.8
      FENDet120.695.894.495.9
      GCBANet948.095.4
      FL-CSE-ROIE107.695.9
      YOLO-CLF95.093.798.263.1
      MCMA-Net84.797.996.6
      Ours2.36.396.194.198.672.2
    • Table 4. Contrast experimental results at different resolutions

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      Table 4. Contrast experimental results at different resolutions

      DatasetInput sizeModelAP_0.5 /%AP_0.5∶0.95 /%
      HRSID320YOLOv877.652.6
      Ours79.053.3
      480YOLOv886.260.1
      Ours88.463.3
      640YOLOv890.565.8
      Ours92.067.6
      SAR-Ship-Dataset320YOLOv892.454.6
      Ours92.655.0
      480YOLOv892.254.0
      Ours92.555.3
      640YOLOv891.153.5
      Ours91.654.8
      SSDD320YOLOv894.066.2
      Ours94.865.1
      480YOLOv897.270.0
      Ours97.470.2
      640YOLOv897.069.1
      Ours98.672.2
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    Changzhen Xiong, Xiyu Li, Heyi Zhao, Songming Xie. Lightweight Multi‑Scale Synthetic Aperture Radar Ship Detection Algorithm[J]. Acta Optica Sinica, 2025, 45(5): 0528003

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Dec. 5, 2024

    Accepted: Jan. 15, 2025

    Published Online: Mar. 17, 2025

    The Author Email: Li Xiyu (lixiyu@mail.ncut.edu.cn)

    DOI:10.3788/AOS241848

    CSTR:32393.14.AOS241848

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