Orientation-Aware Feature Fusion Network for Ship Detection in SAR Images

Abstract

Recently, deep learning methods have been successfully applied to the ship detection in synthetic aperture radar (SAR) images. It is still a great challenge to detect SAR ships, due to the extremely poor image quality and complex background. To solve the problems, a novel method named orientation-aware feature fusion network (OFF-Net) for ship detection in SAR images is proposed in this letter. OFF-Net consists of global context path aggregation (GCPA) module and local rotated contrast enhance (LRCE) module, which fuses the global and local information in feature extraction. First, GCPA module is explored to integrate the global context block with path aggregation network (PAN) to learn the global background information. Second, by designing a rotation scheme based on feature map cyclic shift with four directions, LRCE module is developed to enhance the targets and suppress the background clutters in SAR images. Finally, a decoupled orientation-aware head is proposed to handle the arbitrarily rotated ships more robustly and alleviate the conflict between classification and regression tasks during detection. In addition, a high-resolution SAR-ship detection dataset (OBB-HRSDD) with rotatable bounding boxes is provided. The detection results on the SAR ship detection dataset (SSDD+) and OBB-HRSDD illustrate that our method outperforms all the compared methods. The code and OBB-HRSDD are released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SJX152/papercode</uri>