Volume 11 Issue 3
Jun.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(3): 335-346
XU Changgui, ZHANG Bo, GAO Jianwei, et al. FCOSR: An anchor-free method for arbitrary-oriented ship detection in SAR images[J]. Journal of Radars, 2022, 11(3): 335–346. doi: 10.12000/JR21204
Citation: XU Changgui, ZHANG Bo, GAO Jianwei, et al. FCOSR: An anchor-free method for arbitrary-oriented ship detection in SAR images[J]. Journal of Radars, 2022, 11(3): 335–346. doi: 10.12000/JR21204
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FCOSR: An Anchor-free Method for Arbitrary-oriented Ship Detection in SAR Images

doi: 10.12000/JR21204
  • 1.

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

  • 2.

    College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    Institute of Spacecraft Application System Engineering, CAST, Beijing 100094, China

Funds:  The National Natural Science Foundation of China (41930110, 41901292)
More Information
  • Corresponding author: ZHANG Bo, zhangbo202140@aircas.ac.cn
  • Received Date: 2021-12-16
  • Accepted Date: 2022-02-25
  • Rev Recd Date: 2022-02-22
    • Available Online: 2022-03-05
  • Publish Date: 2022-03-24
    Abstract
  • The anchor-free network represented by a Fully Convolutional One-Stage object detector (FCOS) avoids the hyperparameter setting issue caused by the preset anchor boxes; however, the result of the horizontal bounding boxes cannot indicate the precise boundary and orientation of the arbitrary-oriented ship detection in synthetic-aperture radar images. To solve this problem, this paper proposes a detection algorithm named FCOSR. First, the angle parameter is added to the FCOS regression branch to output the rotatable bounding boxes. Second, 9-point features based on deformable convolution are introduced to predict the ship confidence and the boundary-box residual to reduce the land false alarm and improve the accuracy of the boundary box regression. Finally, in the training stage, the rotatable adaptive sample selection strategy is used to allocate appropriate positive sample points to the real ship to improve the network detection accuracy. Compared to the FCOS and currently published anchor-based rotatable detection networks, the proposed network exhibited faster detection speed and higher detection accuracy on the SSDD+ and HRSID datasets with the mAPs of 91.7% and 84.3%, respectively. The average detection time of image slices was only 33 ms.

     

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