基于特征融合和定位增强的SAR图像舰船目标检测方法

doi:10.12305/j.issn.1001-506X.2025.11.08

Abstract

Abstract:

Due to the inconsistent scale of ship targets in spaceborne synthetic aperture radar （SAR） images and their susceptibility to background clutter noise, the accuracy and localization precision of SAR image ship target detection results are often limited. In view of these problems, a ship target detection algorithm based on feature fusion and localization enhancement is proposed. Firstly, by mining the contextual information between adjacent keys in the feature map, the ability of network to extract the contextual features of ship targets is improved. Secondly, the path aggregation feature pyramid network is adopted to shorten the length of feature transmission paths, which is used to transfer high-resolution positioning feature information, thereby improving the localization accuracy of the target bounding box. Finally, the detection results of the fully convolutional detector and the Transformer detector are fused to alleviate the problem of imbalanced positive and negative sample ratios in SAR images and improve the detection effect of small and medium-sized ship targets. Experimental results based on the measured dataset show that the proposed method has good detection performance and generalization ability in complex backgrounds for ship target.

Key words: synthetic aperture radar (SAR), ship target detection, attention mechanism, feature fusion, target location

CLC Number:

TP 751

Yong WANG, Boya ZHANG. Ship target detection method in SAR images based on feature fusion and location enhancement[J]. Systems Engineering and Electronics, 2025, 47(11): 3586-3597.

Figures/Tables 20

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References 28

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层名称	输出大小	网络结构
层名称	输出大小	ResNet-50	ResNet-101	CFN
Conv1	112×112	7×7，64，步长2	7×7，64，步长2	7×7，64，步长2
Conv2_x	56×56	3×3，最大池化，步长2	3×3，最大池化，步长2	3×3，最大池化，步长2
Conv2_x	56×56	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;64} \\ {3 \times 3,\;64} \\ {1 \times 1,\;256} \end{array}} \right] \times 3$	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;64} \\ {3 \times 3,\;64} \\ {1 \times 1,\;256} \end{array}} \right] \times 3$	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;64} \\ {3 \times 3,\;64} \\ {1 \times 1,\;256} \end{array}} \right] \times 3$
Conv3_x	28×28	$ \left[ {\begin{array}{*{20}{c}} {1 \times 1,\;128} \\ {3 \times 3,\;128} \\ {1 \times 1,\;512} \end{array}} \right] \times 4 $	$ \left[ {\begin{array}{*{20}{c}} {1 \times 1,\;128} \\ {3 \times 3,\;128} \\ {1 \times 1,\;512} \end{array}} \right] \times 4 $	$ \left[ {\begin{array}{*{20}{c}} {1 \times 1,\;128} \\ {3 \times 3,\;128} \\ {1 \times 1,\;512} \end{array}} \right] \times 4 $
Conv4_x	14×14	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;256} \\ {3 \times 3,\;256} \\ {1 \times 1,\;1024} \end{array}} \right] \times 6$	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;256} \\ {3 \times 3,\;256} \\ {1 \times 1,\;1024} \end{array}} \right] \times 23$	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;256} \\ {{\text{CoT}},\;256} \\ {1 \times 1,\;1024} \end{array}} \right] \times 6$
Conv5_x	7×7	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;512} \\ {3 \times 3,\;512} \\ {1 \times 1,\;2048} \end{array}} \right] \times 3$	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;512} \\ {3 \times 3,\;512} \\ {1 \times 1,\;2048} \end{array}} \right] \times 3$	$\left[ {\begin{array}{*{20}{c}} {1 \times 1,\;64} \\ {3 \times 3,\;64} \\ {1 \times 1,\;256} \end{array}} \right] \times 3$

算法	P	R	F₁	AP_0.5	AP_0.75	AP_0.5∶0.95
Faster R-CNN	93.1	90.7	91.8	93.7	77.1	63.2
SSD	88.3	82.8	85.4	88.2	65.1	55.3
YOLOv7	85.8	79.5	82.5	85.5	59.2	52.7
RetinaNet	97.2	83.7	90.0	93.0	76.3	65.0
FFLE-Net	94.9	95.8	95.4	98.5	84.4	71.4

算法	CFN	PAFPN	TCMA	AP_0.5/%	AP_0.75/%	AP_0.5∶0.95/%
消融算法1	−	√	√	95.8	79.1	65.3
FFLE-Net	√	√	√	98.5	84.4	71.4

算法	CFN	PAFPN	TCMA	AP_0.5/%	AP_0.75/%	AP_0.5∶0.95/%
消融算法2	√	−	√	97.4	82.8	69.8
FFLE-Net	√	√	√	98.5	84.4	71.4

算法	CFN	PAFPN	TCMA	AP_0.5/%	AP_0.75/%	AP_0.5∶0.95/%
消融算法3	√	√	−	95.7	75.6	64.4
FFLE-Net	√	√	√	98.5	84.4	71.4

Ship target detection method in SAR images based on feature fusion and location enhancement

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算法	AP_0.5	AP_0.75	AP_0.5∶0.95
RetinaNet	66.6	13.0	25.6
FFLE-Net	70.6	15.3	27.7