A Generating-Anchor Network for Small Ship Detection in SAR Images

Abstract

Synthetic aperture radar (SAR) ship detection especially for small ships has issues, such as dense distribution of ships, interference from land and small islands. To address these issues, many deep learning methods, including anchor-based and anchor-free methods, have been successfully migrated from optical scenes to SAR images. However, when the preset scale of anchors does not match well with the ships, it will seriously reduce the detection precision. Due to the lack of anchor-based refinement process, anchor-free methods may generate missing or false alarms in complex scenarios. In this article, a two-stage ship detection network which can generate anchors is proposed. First, our method generates high-quality anchors by network, which is more beneficial for the network to capture small ships. In addition, the generated anchors are centrally set in the region of ships, which reduces the number of anchors unrelated to ships. Second, the receptive field enhancement module is inserted into the feature pyramid network. It sets different dilation ratios of atrous convolution according to the scale of the feature map, which further enriches the semantic information of the elements in the feature map. Therefore, the network can use the information of a wider region effectively to detect ships. Finally, to verify the effectiveness of our method, extensive experiments are carried out on SAR ship detection dataset and high-resolution SAR images dataset. The results show that our method has more strong ability of detecting small ships, and achieves better detection performance than some state-of-the-art methods.

Keywords

• Deep learning (DL)
• ship detection
• small-scale target
• synthetic aperture radar (SAR)