Volume 12 Issue 5
Oct.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(5): 1097-1111
XU Xiaowo, ZHANG Xiaoling, ZHANG Tianwen, et al. SAR ship detection in complex scenes based on adaptive anchor assignment and IOU supervise[J]. Journal of Radars, 2023, 12(5): 1097–1111. doi: 10.12000/JR23059
Citation: XU Xiaowo, ZHANG Xiaoling, ZHANG Tianwen, et al. SAR ship detection in complex scenes based on adaptive anchor assignment and IOU supervise[J]. Journal of Radars, 2023, 12(5): 1097–1111. doi: 10.12000/JR23059
shu

SAR Ship Detection in Complex Scenes Based on Adaptive Anchor Assignment and IOU Supervise

doi: 10.12000/JR23059
  • 1.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 2.

    School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (61571099)
More Information
  • Corresponding author: ZENG Tianjiao, tzeng@uestc.edu.cn
  • Received Date: 2023-04-27
  • Rev Recd Date: 2023-05-26
    • Available Online: 2023-05-31
  • Publish Date: 2023-06-21
    Abstract
  • This study aims to address the unreasonable assignment of positive and negative samples and poor localization quality in ship detection in complex scenes. Therefore, in this study, a Synthetic Aperture Radar (SAR) ship detection network (A3-IOUS-Net) based on adaptive anchor assignment and Intersection over Union (IOU) supervise in complex scenes is proposed. First, an adaptive anchor assignment mechanism is proposed, where a probability distribution model is established to adaptively assign anchors as positive and negative samples to enhance the ship samples’ learning ability in complex scenes. Then, an IOU supervise mechanism is proposed, which adds an IOU prediction branch in the prediction head to supervise the localization quality of detection boxes, allowing the network to accurately locate the SAR ship targets in complex scenes. Furthermore, a coordinate attention module is introduced into the prediction branch to suppress the background clutter interference and improve the SAR ship detection accuracy. The experimental results on the open SAR Ship Detection Dataset (SSDD) show that the Average Precision (AP) of A3-IOUS-Net in complex scenes is 82.04%, superior to the other 15 comparison models.

     

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