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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2022, Vol. 43 ›› Issue (S1): 726922-726922.doi: 10.7527/S1000-6893.2022.26922

• Information Fusion • Previous Articles     Next Articles

A lightweight oriented ship detection method in SAR images

SU Hang1, XU Congan1,2, YAO Libo1, LI Jianwei3, LING Qing1, GAO Long1   

  1. 1. Institute of Information Fusion, Naval Aviation University, Yantai 264000, China;
    2. Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China;
    3. Troops of 92877, Zhoushan 316000, China
  • Received:2022-01-10 Revised:2022-02-10 Published:2022-04-12
  • Supported by:
    National Natural Science Foundation of China (61790550, 61790554, 61971432, 62022092);Young Elite Scientists Sponsorship Program by China Association for Science and Technology (2020-JCJQ-QT-011)

Abstract: Current oriented ship detection methods in Synthetic Aperture Radar (SAR) images based on deep learning cannot meet the requirements of real-time detection. This paper proposes a lightweight oriented ship detection method. A lightweight network structure is designed based on the anchor-free framework. The number of model parameters and running speed are optimized, so that the model can be directly trained from scratch. To solve the problem of angle sensitivity existing in the method based on angle regression, an oriented bounding box representation method is proposed based on the rotated vector. Experiments are conducted on public SAR ship detection dataset. The results show that the proposed method can reduce model parameters and improve the detection speed while maintaining the detection accuracy, which fully verifies the effectiveness of the method.

Key words: SAR image, oriented ship detection, lightweight, train from scratch, rotated vector

CLC Number: