基于全局位置信息和残差特征融合的SAR船舶检测算法

doi:10.12305/j.issn.1001-506X.2024.03.09

Abstract

Abstract:

In view of the problem that ship targets in synthetic aperture radar (SAR) images have varying scales and are easily affected by sea clutter, ground clutter, and coherent speckle noise, which makes it difficult to extract target multidimensional features and leads to semantic ambiguity during feature fusion, resulting in low ship target detection rate and high false alarm rate, a SAR ship target detection algorithm based on global position information and residual feature fusion is proposed. Based on the Faster region convolutional neural network (R-CNN) object detection algorithm, improvements are made in the feature extraction network and feature fusion network. A height-width attention mechanism is used in the feature extraction network to extract the global position information of the target in the image, enhancing the multidimensional feature extraction capability of the target. A bidirectional feature pyramid network with residual connections is used in the feature fusion network to reduce semantic ambiguity in the feature fusion process, reduce false alarms of ship targets in complex backgrounds, and perform bidirectional fusion of multi-scale features at different levels to enhance the connection between high and low-level features and improve the detection capability of multi-scale ship targets. The algorithm achieves a mean average precision of 98.2% on the SAR ship dataset, surpassing some algorithms by 2.4% or more. The experiments show that the proposed algorithm effectively extracts multidimensional features of the target, significantly alleviates semantic ambiguity problems, and has good detection and generalization capabilities.

Key words: synthetic aperture radar (SAR), ship detection, attention mechanism, feature pyramid network, residual connection

CLC Number:

TN95

Xiaoyu FANG, Lijia HUANG. SAR ship detection algorithm based on global position information and fusion of residual feature[J]. Systems Engineering and Electronics, 2024, 46(3): 839-848.

Figures/Tables 15

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α	mAP50/%
0(不添加残差连接)	93.6
0.25	95.3
0.5	96.5
1	95.6
2	91.5
自适应调整	不稳定

算法	mAP/%	mAP50/%	mAP75/%	小目标/%	中目标/%	大目标/%	速度/(iter/s)
Faster R-CNN	53.1	93.6	57.2	51.2	61.2	56.5	9.6
ImShips	50.4	93.1	54.3	51.4	54.7	58.6	15.5
QUAD-FPN	56.3	95.8	64.4	54.6	63.2	70.3	5.7
SSDNet	58.3	98.2	69.0	57.3	65.5	72.5	8.1

算法	mAP	mAP50	mAP75	小目标	中目标	大目标
Faster R-CNN	53.1	93.6	57.2	51.2	61.2	56.5
HWAM	58.3	97.0	65.0	55.5	63.0	70.0
ResFPN	54.7	96.5	61.7	56.2	62.3	75.1
HWAM+ResFPN	58.3	98.2	69.0	57.3	65.5	72.5

卷积结构	mAP50
3×3卷积	93.6
改良Inception	95.4

算法	mAP	mAP50	mAP75	小目标	中目标	大目标
Faster R-CNN	26.5	62.0	15.3	6.2	28.5	35.9
ImShips	32.4	68.5	26.6	8.7	35.3	39.5
QUAD-FPN	36.3	74.4	27.8	14.8	37.2	58.5
SSDNet	37.1	80.2	29.0	15.8	37.9	64.6

SAR ship detection algorithm based on global position information and fusion of residual feature

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