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.
SU Hang
,
XU Congan
,
YAO Libo
,
LI Jianwei
,
LING Qing
,
GAO Long
. A lightweight oriented ship detection method in SAR images[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022
, 43(S1)
: 726922
-726922
.
DOI: 10.7527/S1000-6893.2022.26922
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