基于锚框自适应和多尺度增强的SAR舰船检测

doi:10.12305/j.issn.1001-506X.2024.04.08

Abstract

Abstract:

Aiming at the problem that the scales of synthetic aperture radar (SAR) ship detection anchors are fixed, and the performance of multi-scale detection is poor, a SAR ship detection method based on adaptive anchors multi-scale enhancement network(AA-MSE-Net) is proposed. Firstly, AA-MSE-Net introduces adaptive anchors mechanism to generate high quality anchors that adapt to the target shape, which enhances the ability to describe the target shape. Secondly, AA-MSE-Net proposes a multi-scale enhancement feature pyramid network to fusion enhanced multi-scale features, thus enhance the multi-scale description ability of the network. Finally, AA-MSE-Net introduces deformable convolution in the backbone to extract ship deformation features and further improve detection accuracy. Experiments show that the average precision of AA-MSE-Net on the public SAR ship detection dataset is higher than that of eight comparison methods.

Key words: synthetic aperture radar (SAR), ship detection, adaptive anchor, scale enhancement

CLC Number:

TP751

Zikang SHAO, Xiaoling ZHANG, Tianwen ZHANG, Tianjiao ZENG. SAR ship detection based on adaptive anchor and multi-scale enhancement[J]. Systems Engineering and Electronics, 2024, 46(4): 1204-1211.

Figures/Tables 12

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References 41

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方法	Recall	Precision	AP
Faster RCNN^[16]	90.44	87.08	89.74
YOLOv3^[11]	76.10	97.18	75.68
HR-SDNet^[38]	90.99	96.49	90.82
Cascade RCNN^[17]	90.81	94.10	90.50
Libra RCNN^[31]	91.73	86.78	90.88
Double-Head RCNN^[39]	91.91	86.96	91.10
Free-Anchor^[40]	92.65	72.31	91.04
Grid RCNN^[41]	89.71	87.77	88.92
AA-MSE-Net	93.41	87.18	92.97

自适应锚框	MSE-FPN	D-Conv	Recall	Precision	AP
×	×	×	90.44	87.08	89.74
√	×	×	92.12	82.59	91.29
√	√	×	92.31	82.22	91.66
√	√	√	93.41	87.18	92.97

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[12]	Xianghai LI, Zhiwei YANG, Shun HE, Guisheng LIAO, Chaolei HAN, Yan JIANG. Method for SAR-GMTI moving target radial velocity estimation and relocation based on road network information assistance in multi-satellite formation system [J]. Systems Engineering and Electronics, 2023, 45(3): 629-637.
[13]	Yongzhen LI, Yemin LIU, Shiqi XING, Datong HUANG, Zhengwen LU. Study on passive direction-finding and SAR deception jamming method based on multi-receiver cooperation [J]. Systems Engineering and Electronics, 2023, 45(12): 3792-3805.
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[15]	Chensong TAO, Siwei CHEN, Shunping XIAO. SAR image interrupted sampling repeater jamming detection based on deep learning models [J]. Systems Engineering and Electronics, 2023, 45(11): 3465-3473.

SAR ship detection based on adaptive anchor and multi-scale enhancement

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