A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation
release_hvvlcaiavnfhhkqn4g7qeic5ie
by
Tianwen Zhang, Xiaoling Zhang
2022
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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