LAUSR

Ship detection is a challenging task in the synthetic aperture radar (SAR) automatic target recognition due to the large aspect ratio, arbitrary orientation, and dense arrangement of ships and severe background interference in the inshore scenes. Although considerable progress has been made in recent research, there have still been certain challenges in achieving fast and efficient detection of arbitrary-oriented ships in SAR images. To address these challenges, this article proposes a single-stage detection method based on multiscale feature fusion and calibration. The proposed detection method can detect arbitrary-oriented ships in SAR images with high accuracy and speed. Specifically, a head network with the stepwise regression from the coarse- to fine-grained detection is designed to detect arbitrary-oriented ships accurately. In addition, a feature enhancement module is constructed to fuse and refine shallow texture features and deep semantic features, aiming to obtain multiscale fusion features containing sufficient contextual information. Finally, an attention module is used to calibrate multiscale fusion features to highlight the ship information while suppressing interference from the surrounding background. The effectiveness of the proposed method is verified by experiments on two public SAR ship datasets and a panoramic SAR image. The experimental results show that compared with the other rotation detectors, the proposed detection method has competitive detection results and can achieve state-of-the-art detection performance.