AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical
  Attention Network
Xiaolin Ma Junkai Cheng Aihua Li Yuhua Zhang Zhilong Lin
Recently, methods based on deep learning have been successfully applied to
ship detection for synthetic aperture radar (SAR) images. Despite the
development of numerous ship detection methodologies, detecting small and
coastal ships remains a significant challenge due to the limited features and
clutter in coastal environments. For that, a novel adaptive multi-hierarchical
attention module (AMAM) is proposed to learn multi-scale features and
adaptively aggregate salient features from various feature layers, even in
complex environments. Specifically, we first fuse information from adjacent
feature layers to enhance the detection of smaller targets, thereby achieving
multi-scale feature enhancement. Then, to filter out the adverse effects of
complex backgrounds, we dissect the previously fused multi-level features on
the channel, individually excavate the salient regions, and adaptively
amalgamate features originating from different channels. Thirdly, we present a
novel adaptive multi-hierarchical attention network (AMANet) by embedding the
AMAM between the backbone network and the feature pyramid network (FPN).
Besides, the AMAM can be readily inserted between different frameworks to
improve object detection. Lastly, extensive experiments on two large-scale SAR
ship detection datasets demonstrate that our AMANet method is superior to
state-of-the-art methods.