Ship Target Detection in SAR Image Based on Selective Coordinate Attention

YAN Chun-man; WANG Cheng

doi:10.12263/DZXB.20211416

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Ship Target Detection in SAR Image Based on Selective Coordinate Attention

PAPERS | 更新时间：2025-12-08

- Ship Target Detection in SAR Image Based on Selective Coordinate Attention
- ACTA ELECTRONICA SINICA Vol. 51, Issue 9, Pages: 2481-2491(2023)
- 作者机构：
  
  1.西北师范大学物理与电子工程学院，甘肃兰州 730070
  2.甘肃省智能信息技术与应用工程研究中心，甘肃兰州 730070
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61961037);Industrial Support Plan of Education Department of Gansu Province(2021CYZC-30)
- DOI：10.12263/DZXB.20211416
  CLC： TN957.52
- Received：18 October 2021，
  
  Revised：2022-10-25，
  
  Published：25 September 2023
- 稿件说明：
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严春满,王铖.基于选择性坐标注意力的SAR图像舰船目标检测[J].电子学报,2023,51(09):2481-2491.

YAN Chun-man,WANG Cheng.Ship Target Detection in SAR Image Based on Selective Coordinate Attention[J].ACTA ELECTRONICA SINICA,2023,51(09):2481-2491.
严春满,王铖.基于选择性坐标注意力的SAR图像舰船目标检测[J].电子学报,2023,51(09):2481-2491. DOI： 10.12263/DZXB.20211416.

YAN Chun-man,WANG Cheng.Ship Target Detection in SAR Image Based on Selective Coordinate Attention[J].ACTA ELECTRONICA SINICA,2023,51(09):2481-2491. DOI： 10.12263/DZXB.20211416.

摘要

针对SAR（Synthetic Aperture Radar）图像舰船目标检测结果虚警率和漏检率较高的问题，本文提出一种基于选择性坐标注意力机制的舰船目标检测算法. 该算法以新的选择性坐标注意力机制为基础，首先通过不同卷积核的特征提取分支对舰船目标进行特征提取；然后融合所有分支的特征，并沿融合后特征的不同空间方向进行编码形成两个一维特征向量，以捕获空间方向上特征的位置信息；最后利用这一对方向和位置敏感的特征向量编码形成“门”机制，对各分支不同大小感受野提取的特征选择性地加权融合，以增强舰船目标的特征表示. 本文以SSD（Single Shot MultiBox Detector）作为基础检测算法首先在SSDD（SAR Ship Detection Dataset）数据集上进行实验，实验结果表明，选择性坐标注意力机制相较于其他注意力机制能有效提升网络模型对舰船目标的检测能力，同时，基于选择性坐标注意力机制改进的SSD舰船目标检测算法平均检测精度达到了94.20%，较原SSD算法提升了4.45%. 此外，通过在其他两个舰船数据集上的进一步测试，反映改进算法具有较好的泛化性，其综合性能优于其他对比目标检测算法.

Abstract

Aiming at the high false alarm rate and missed detection rate of ship target detection results in SAR (Synthetic Aperture Radar) images

a ship target detection algorithm based on selective coordinate attention mechanism is proposed in this paper. The algorithm is based on a new selective coordinate attention mechanism. Firstly

the feature of ship target is extracted by the feature extraction branches of different convolution kernels. Then

the features of all branches are fused

and in order to capture the position information of the features in the spatial direction

the features are encoded along different spatial directions of the fused features to form two one-dimensional feature vector codes. Finally

this direction and position sensitive feature vector coding is used to form a “gate” mechanism to get the weighted fusion of the features extracted from receptive fields with different sizes of each branch

so as to enhance the feature representation of ship targets. In this paper

the SSD (Single Shot MultiBox Detector) algorithm is used as a benchmark to test the detection results of ship targets on the SSDD (SAR Ship Detection Dataset) data set. The experimental results show that

compared with other attention mechanisms

the selective coordinate attention mechanism improves the ship detection ability of the network model more effectively. At the same time

the average detection accuracy of the SSD algorithm based on selective coordinate attention mechanism is improved to 94.20%

which is 4.45% higher than the original SSD algorithm. In addition

further tests on the other two ship data sets show that the improved algorithm has good generalization and its comprehensive performance is better than the comparison algorithms.

关键词

Keywords

references

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