上下文信息融合与分支交互的SAR图像舰船无锚框检测

曲海成; 高健康; 刘万军; 王晓娜

doi:10.11999/JEIT201059

上下文信息融合与分支交互的SAR图像舰船无锚框检测

doi: 10.11999/JEIT201059

辽宁工程技术大学软件学院葫芦岛 125105

基金项目: 国家自然科学基金青年基金(41701479)，辽宁省教育厅基金(LJ2019JL010)，辽宁工程技术大学学科创新团队(LNTU20TD-23)

详细信息

作者简介:
曲海成：男，1981年生，副教授，研究方向为遥感影像高性能计算、视觉信息计算、目标检测与识别

高健康：男，1996年生，硕士生，研究方向为遥感图像目标检测

刘万军：男，1959年生，教授，研究方向为数字图像处理、运动目标检测与跟踪

王晓娜：女，1994年生，硕士生，研究方向为数字图像处理

通讯作者:
高健康　gjk_0825@163.com

¹⁾ SSDD数据集下载链接：https://zhuanlan.zhihu.com/p/143794468²⁾ SAR-Ship-Dataset数据集下载：https://pan.baidu.com/s/1PhSMkXVcuRM8M8xL15iBIQ
中图分类号: TN911.73; TP751
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- 被引次数: 0
出版历程
- 收稿日期: 2020-12-16
- 修回日期: 2021-05-27
- 网络出版日期: 2021-08-27
- 刊出日期: 2022-01-10

An Anchor-free Method Based on Context Information Fusion and Interacting Branch for Ship Detection in SAR Images

School of Software, Liaoning Technical University, Huludao 125105, China

Funds: The Young Scientists Fund of National Natural Science Foundation of China (41701479), The Department of Education Fund Item (LJ2019JL010) of Liaoning Province, The Discipline Innovation Team of Liaoning Technical University (LNTU20TD-23)

摘要

摘要: SAR图像中舰船目标稀疏分布、锚框的设计，对现有基于锚框的SAR图像目标检测方法的精度和泛化性有较大影响，因此该文提出一种上下文信息融合与分支交互的SAR图像舰船目标无锚框检测方法，命名为CI-Net。考虑到SAR图中舰船尺度的多样性，在特征提取阶段设计上下文融合模块，以自底向上的方式融合高低层信息，结合目标上下文信息，细化提取到的待检测特征；其次，针对复杂场景中目标定位准确性不足的问题，提出分支交互模块，在检测阶段利用分类分支优化回归分支的检测框，改善目标定位框的精准性，同时将新增的IOU分支作用于分类分支，提高检测网络分类置信度，抑制低质量的检测框。实验结果表明：在公开的SSDD和SAR-Ship-Dataset数据集上，该文方法均取得了较好的检测效果，平均精度(AP)分别达到92.56%和88.32%，与其他SAR图舰船检测方法相比，该文方法不仅在精度上表现优异，在摒弃了与锚框有关的复杂计算后，较快的检测速度，对SAR图像实时目标检测也有一定的现实意义。
- 合成孔径雷达 /
- 舰船检测 /
- 无锚框 /
- 上下文信息 /
- 自注意力
Abstract: Ship targets are sparsely distributed in Synthetic Aperture Radar (SAR) images, and the design of anchor frame has a great impact on the accuracy and generalization of existing SAR image target detection method based on anchor. Therefore, an anchor-free method based on context information fusion and interacting branch for ship detection in SAR images (named as CI-Net) is proposed. Considering the diversity of ship scale in SAR images, a context fusion module is designed in the feature extraction stage, integrate high and low levels of information in a bottom-up manner and refine the extracted features to be detected by combining with the target context information. Secondly, aiming at the problem of complex targets in the scene is not accurate, interacting branch module is put forward. In the detection phase, use classification branches optimization regression testing box is used, to improve the target frame’s precision. At the same time, the new Intersection over Union (IOU) is used on branches of the classification to improve detection network classification confidence, to inhibit detection box of low quality. Experimental results show that the proposed method achieves good detection results on both SSDD and SAR-Ship-Dataset, with Average Precision (AP) reaching 92.56% and 88.32%, respectively. Compared with other ship detection methods in SAR image, the proposed method not only has excellent performance in accuracy, but also has a faster detection speed after abandoning the complex calculation related to anchor frame. It also has a certain practical significance for real-time target detection in SAR image.
- Synthetic Aperture Radar (SAR) /
- Ship detection /
- Anchor-free /
- Context information /
- Self-attention
¹⁾ SSDD数据集下载链接：https://zhuanlan.zhihu.com/p/143794468²⁾ SAR-Ship-Dataset数据集下载：https://pan.baidu.com/s/1PhSMkXVcuRM8M8xL15iBIQ

HTML全文

图 1 无锚框的检测模型

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图 2 CI-Net检测模型框架

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图 3 上下文融合模块

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图 4 GCNet结构

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图 5 自注意力模块

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图 6 检测结果对比图

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图 7 上下文融合模块特征可视化

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图 8 不同方法的P-R曲线图

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表 1 舰船数据集的基本信息

数据集	传感器来源	空间分辨率(m)	极化方式	输入图像大小	场景
SSDD	RadarSat-2, TerraSAR-X, Sentinel-1	1～15	VV, HH, VH, HV	500×500	近海、近岸区域
SAR-Ship Dataset	GF-3, Sentinel-1	3, 5, 8, 10等	VV, HH, VH, HV	256×256	远海区域

下载: 导出CSV

表 2 模型实验结果

方法	上下文融合(CF)	分支交互(IB)	召回率(%)	准确率(%)	平均精度(%)	F₁(%)	fps
FCOS^[14]	×	×	88.64	88.44	86.27	88.54	23
本文	√	×	92.23	86.60	90.69	89.32	29
FCOS^[14]	×	√	90.31	93.41	88.42	91.83	22
本文	√	√	94.27	92.04	92.56	93.14	28
注：“×”表示没有采用该模块。“√”表示采用该模块。加粗值为每列最优结果。

下载: 导出CSV

表 3 不同方法在SSDD数据集上检测性能对比

方法	单阶段	无锚框	召回率(%)	准确率(%)	平均精度(%)	F₁(%)	fps
Faster R-CNN	×	×	85.39	84.18	83.07	84.78	11
RetinaNet	√	×	89.40	90.43	87.94	89.91	16
DCMSNN	×	×	91.59	88.33	89.34	89.93	8
本文CI-Net	√	√	94.27	92.04	92.56	93.14	28

下载: 导出CSV

表 4 不同方法在SAR-Ship-Dataset上检测性能对比

方法	单阶段	无锚框	召回率(%)	准确率(%)	平均精度(%)	F₁(%)	fps
Faster R-CNN	×	×	84.30	84.47	81.77	84.39	13
RetinaNet	√	×	84.60	85.83	82.02	85.21	21
DCMSNN	×	×	86.64	88.07	84.36	87.35	9
本文CI-Net	√	√	90.28	88.14	88.32	89.20	34

下载: 导出CSV

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