基于全局特征融合的SAR图像旋转舰船目标检测算法

doi:10.12305/j.issn.1001-506X.2024.12.13

Abstract

Abstract:

Conventional models are not effective in detecting inshore rotated ship targets in synthetic aperture radar (SAR) images. To solve this problem, a method of detecting rotated ship targets in SAR images based on global feature fusion is proposed. Firstly, the global attention feature pyramid network is used to fuse features of different levels, which shortens the transmission path from the bottom feature to the top feature. Secondly, positional embedding is added in the image block fusion stage to reduce the loss of location information caused by down-sampling. Finally, the rotated feature alignment network is used to generate high-quality anchor points and rotated alignment features for classification and coordinate regression. The proposed method achieves an average detection precision of 0.894 8 on the rotated ship detection dataset in SAR images (RSDD-SAR) dataset when the rotated intersection over union (IoU) is 0.5. The proposed method has good detection performance for both inshore and offshore ships.

Key words: synthetic aperture radar (SAR), ship detection, rotated target detection, neural network, feature fusion

CLC Number:

TP751.1

Fengtao XUE, Tianyu SUN, Yimin YANG, Jian YANG. Rotated ship target detection algorithm in SAR images based on global feature fusion[J]. Systems Engineering and Electronics, 2024, 46(12): 4044-4053.

Figures/Tables 17

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References 30

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混淆矩阵		真实类别
混淆矩阵		正样本	负样本
预测类别	正样本	TP	FP
预测类别	负样本	FN	TN

IoU	P	R	AP
0.50	0.886	0.896	0.895
0.60	0.868	0.799	0.842
0.70	0.777	0.711	0.734
0.80	0.612	0.404	0.449
0.90	0.391	0.255	0.231

方法	骨干网络	AP₅₀	运算次数	每秒帧数	参数量大小
Rotated Faster RCNN	ResNet50	0.831 5	63.26	62.4	41.14
Rotated RetinaNet	ResNet50	0.861 4	52.39	75.4	36.13
Oriented RCNN	ResNet50	0.883 7	63.28	62.4	41.13
S2ANet	ResNet50	0.873 2	49.05	80.5	38.76
Rotated FCOS	ResNet50	0.859 5	51.55	76.6	32.12
R3Det	ResNet50	0.812 2	82.17	48.1	41.81
本文方法	Swin-Tiny	0.894 8	79.06	49.9	58.65

骨干网络	特征融合	位置编码	AP
ResNet50	-	-	0.869 2
Swin-Tiny	-	-	0.882 5
Swin-Tiny	√	-	0.891 4
Swin-Tiny	√	√	0.894 8

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Rotated ship target detection algorithm in SAR images based on global feature fusion

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