A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation

• URL: http://arxiv.org/abs/2207.03912v1
• Date: Fri, 8 Jul 2022 14:04:04 GMT
• Title: A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation
• Authors: Tianwen Zhang, and Xiaoling Zhang
• Abstract summary: 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.
• Score: 4.232332676611087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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