Global Context Fusion Network for SAR Ship Detection

Abstract

Ship detection in synthetic aperture radar (SAR) image is crucial for marine surveillance and navigation. The application of detection network based on deep learning has achieved a promising result in SAR ship detection. However, the existing networks encounters challenges due to the complex backgrounds, diverse scales and irregular distribution of ship targets. To address these issues, this article proposes a detection algorithm that integrates global context of the images (GCF-Net). First, we construct a global feature extraction module in the backbone network of GCF-Net, which encodes features along different spatial directions. Then, we incorporate bi-directional feature pyramid network (BiFPN) in the neck network to fuse the multi-scale features selectively. Finally, we design a convolution and transformer mixed (CTM) detection head to obtain contextual information of targets and concentrate network attention on the most informative regions of the images. Experimental results demonstrate that the proposed method achieves more accurate detection of ship targets in SAR images.

Keywords

synthetic aperture radar (SAR) / ship detection / global context fusion / convolutional neural network / feature extraction