论文标题
SAR Ship实例细分的掩盖注意力互动和规模增强网络
A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation
论文作者
论文摘要
现有的大多数合成孔径雷达（SAR）船舶内部分割模型无法实现掩模间间隔或提供有限的相互作用性能。此外，他们的多尺度船舶实例细分性能是中等的，特别是对于小型船只。为了解决这些问题，我们为SAR Ship实例细分提出了掩盖注意力相互作用和规模增强网络（MAI-SE-NET）。 MAI使用非常空间的Pyra-Mid合并（ASPP）来获得多分辨率的功能重新发起，非本地块（NLB）来模拟远距离SPA-TIAL依赖性，并进行串联冲水注意力块（CSAB）以提高相互作用益处。 SE使用内容感知的功能块（CARAFEB）重新组装来产生额外的金字塔底部底层，以提高小型船舶性能，功能平衡操作（FBO）以改善规模功能描述以及全球上下文块（GCB）以完善功能。两个公共SSDD和HRSID数据集的实验结果表明，MAI-SE-NET的表现优于其他九个竞争模型，比SSDD上的4.7％检测AP和3.4％的细分AP和3.0％的检测AP和2.4％的HRSID分割AP，优于次优模型的其他九个竞争模型。
Most of existing synthetic aperture radar (SAR) ship in-stance segmentation models do not achieve mask interac-tion or offer limited interaction performance. Besides, their multi-scale ship instance segmentation performance is moderate especially for small ships. To solve these problems, we propose a mask attention interaction and scale enhancement network (MAI-SE-Net) for SAR ship instance segmentation. MAI uses an atrous spatial pyra-mid pooling (ASPP) to gain multi-resolution feature re-sponses, a non-local block (NLB) to model long-range spa-tial dependencies, and a concatenation shuffle attention block (CSAB) to improve interaction benefits. SE uses a content-aware reassembly of features block (CARAFEB) to generate an extra pyramid bottom-level to boost small ship performance, a feature balance operation (FBO) to improve scale feature description, and a global context block (GCB) to refine features. Experimental results on two public SSDD and HRSID datasets reveal that MAI-SE-Net outperforms the other nine competitive models, better than the suboptimal model by 4.7% detec-tion AP and 3.4% segmentation AP on SSDD and by 3.0% detection AP and 2.4% segmentation AP on HRSID.
