Rotated ship target detection algorithm in SAR images based on global feature fusion

Conventional models are not effective in detecting inshore rotated ship targets in synthetic aperture radar (SAR) images. To solve this problem, a method of detecting rotated ship targets in SAR images based on global feature fusion is proposed. Firstly, the global attention feature pyramid network is used to fuse features of different levels, which shortens the transmission path from the bottom feature to the top feature. Secondly, positional embedding is added in the image block fusion stage to reduce the loss of location information caused by down-sampling. Finally, the rotated feature alignment network is used to generate high-quality anchor points and rotated alignment features for Classification and coordinate regression. The proposed method achieves an average detection precision of 0. 894 8 on the rotated ship detection dataset in SAR images (RSDD-SAR) dataset when the rotated intersection over union (IoU) is 0. 5. The proposed method has good detection Performance for both inshore and offshore ships. © 2024 Chinese Institute of Electronics. All rights reserved.