基于选择性坐标注意力的SAR图像舰船目标检测

严春满; 王铖

doi:10.12263/DZXB.20211416

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基于选择性坐标注意力的SAR图像舰船目标检测

学术论文 | 更新时间：2025-12-08

- 基于选择性坐标注意力的SAR图像舰船目标检测
- Ship Target Detection in SAR Image Based on Selective Coordinate Attention
- 电子学报 2023年51卷第9期页码：2481-2491
- 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：
  
  [ "严春满男，1970年出生，甘肃人. 西北师范大学副教授，硕士生导师.主要研究领域为模式识别、神经网络、深度学习及其在计算机视觉中的应用. E-mail: yancha02@163.com" ]
  [ "王铖男，1997年出生，山东人. 西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为深度学习、嵌入式系统与应用等.E-mail: 610487244@qq.com" ]
- 基金信息：
  
  国家自然科学基金(61961037);甘肃省教育厅产业支撑计划项目(2021CYZC-30)
- DOI：10.12263/DZXB.20211416
  中图分类号： TN957.52
- 收稿：2021-10-18，
  
  修回：2022-10-25，
  
  纸质出版：2023-09-25
- 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

彭书娟 , 曲长文 , 李健伟 . K近邻优化估计的SAR图像建模与目标检测算法 [J]. 控制与决策 , 2020 , 35 ( 9 ): 2199 - 2206 .

PENG S J , QU C W , LI J W . K nearest neighbors optimized estimation algorithm for SAR image statistical modeling and target detection [J]. Control and Decision , 2020 , 35 ( 9 ): 2199 - 2206 . (in Chinese)

WACKERMAN C C , FRIEDMAN K S , PICHEL W G , et al . Automatic detection of ships in RADARSAT-1 SAR imagery [J]. Canadian Journal of Remote Sensing , 2001 , 27 ( 5 ): 568 - 577 .

唐沐恩 , 林挺强 , 文贡坚 . 遥感图像中舰船检测方法综述 [J]. 计算机应用研究 , 2011 , 28 ( 1 ): 29 - 36 .

TANG M E , LIN T Q , WEN G J . Overview of ship detection methods in remote sensing image [J]. Application Research of Computers , 2011 , 28 ( 1 ): 29 - 36 . (in Chinese)

QIN X X , ZHOU S L , ZOU H X , et al . A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images [J]. IEEE Geoscience and Remote Sensing Letters , 2013 , 10 ( 4 ): 806 - 810 .

程旭 , 宋晨 , 史金钢 , 等 . 基于深度学习的通用目标检测研究综述 [J]. 电子学报 , 2021 , 49 ( 7 ): 1428 - 1438 .

CHENG X , SONG C , SHI J G , et al . A survey of generic object detection methods based on deep learning [J]. Acta Electronica Sinica , 2021 , 49 ( 7 ): 1428 - 1438 . (in Chinese)

GIRSHICK R . Fast R-CNN [C]// 2015 IEEE International Conference on Computer Vision (ICCV) . Piscataway : IEEE , 2016 : 1440 - 1448 .

REN S Q , HE K M , GIRSHICK R , et al . Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once: Unified, real-time object detection [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2016 : 779 - 788 .

REDMON J , FARHADI A . YOLO9000: Better, faster, stronger [C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2017 : 6517 - 6525 .

REDMON J , FARHADI A . YOLOv3: An incremental improvement [EB/OL]. ( 2018-04-08 )[ 2021-09-18 ]. https://arxiv.org/abs/1804.02767 https://arxiv.org/abs/1804.02767 .

BOCHKOVSKIY A , WANG C Y , LIAO H Y M . YOLOv4: Optimal speed and accuracy of object detection [EB/OL]. ( 2020-04-23 )[ 2021-09-18 ]. https://arxiv.org/abs/ 2004.10934 https://arxiv.org/abs/2004.10934 .

LIU W , ANGUELOV D , ERHAN D , et al . SSD: Single shot MultiBox detector [C]// Computer Vision—ECCV 2016 . Cham : Springer International Publishing , 2016 : 21 - 37 .

HU J , SHEN L , SUN G . Squeeze-and-excitation networks [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 7132 - 7141 .

LI X , WANG W H , HU X L , et al . Selective kernel networks [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 510 - 519 .

HOU Q B , ZHOU D Q , FENG J S . Coordinate attention for efficient mobile network design [C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2021 : 13708 - 13717 .

WOO S , PARK J , LEE J Y , et al . CBAM: Convolutional block attention module [C]// Computer Vision—ECCV 2018 . Cham : Springer International Publishing , 2018 : 3 - 19 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [EB/OL]. ( 2017-06-12 )[ 2021-09-18 ]. https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762 .

WANG R , SHAO S H , AN M Y , et al . Soft thresholding attention network for adaptive feature denoising in SAR ship detection [J]. IEEE Access , 2021 , 9 : 29090 - 29105 .

ZHAO J P , ZHANG Z H , YU W X , et al . A cascade coupled convolutional neural network guided visual attention method for ship detection from SAR images [J]. IEEE Access , 2018 , 6 : 50693 - 50708 .

CUI Z Y , LI Q , CAO Z J , et al . Dense attention pyramid networks for multi-scale ship detection in SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 11 ): 8983 - 8997 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2016 : 770 - 778 .

WEI S J , ZENG X F , QU Q Z , et al . HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation [J]. IEEE Access , 2020 , 8 : 120234 - 120254 .

WANG Y Y , WANG C , ZHANG H , et al . Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery [J]. Remote Sensing , 2019 , 11 ( 5 ): 531 .

SELVARAJU R R , COGSWELL M , DAS A , et al . Grad-CAM: Visual explanations from deep networks via gradient-based localization [C]// 2017 IEEE International Conference on Computer Vision (ICCV) . Piscataway : IEEE , 2017 : 618 - 626 .

LIN T Y , GOYAL P , GIRSHICK R , et al . Focal loss for dense object detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020 , 42 ( 2 ): 318 - 327 .

LIU S T , HUANG D , WANG Y H . Receptive field block net for accurate and fast object detection [C]// Computer Vision—ECCV 2018 . Cham : Springer International Publishing , 2018 : 404 - 419 .

FU C Y , LIU W , RANGA A , et al . DSSD: Deconvolutional single shot detector [EB/OL]. ( 2017-01-23 )[ 2021-09-18 ]. https://arxiv.org/abs/1701.06659 https://arxiv.org/abs/1701.06659 .

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