上下文信息融合與分支交互的SAR圖像艦船無錨框檢測

曲海成; 高健康; 劉萬軍; 王曉娜

doi:10.11999/JEIT201059

上下文信息融合與分支交互的SAR圖像艦船無錨框檢測

doi: 10.11999/JEIT201059 cstr: 32379.14.JEIT201059

遼寧工程技術(shù)大學軟件學院葫蘆島 125105

基金項目: 國家自然科學基金青年基金(41701479)，遼寧省教育廳基金(LJ2019JL010)，遼寧工程技術(shù)大學學科創(chuàng)新團隊(LNTU20TD-23)

詳細信息

作者簡介:
曲海成：男，1981年生，副教授，研究方向為遙感影像高性能計算、視覺信息計算、目標檢測與識別

高健康：男，1996年生，碩士生，研究方向為遙感圖像目標檢測

劉萬軍：男，1959年生，教授，研究方向為數(shù)字圖像處理、運動目標檢測與跟蹤

王曉娜：女，1994年生，碩士生，研究方向為數(shù)字圖像處理

通訊作者:
高健康　gjk_0825@163.com

¹⁾ SSDD數(shù)據(jù)集下載鏈接：https://zhuanlan.zhihu.com/p/143794468²⁾ SAR-Ship-Dataset數(shù)據(jù)集下載：https://pan.baidu.com/s/1PhSMkXVcuRM8M8xL15iBIQ
中圖分類號: TN911.73; TP751
計量
- 文章訪問數(shù): 1100
- HTML全文瀏覽量: 488
- PDF下載量: 101
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2020-12-16
- 修回日期: 2021-05-27
- 網(wǎng)絡(luò)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圖像中艦船目標稀疏分布、錨框的設(shè)計，對現(xiàn)有基于錨框的SAR圖像目標檢測方法的精度和泛化性有較大影響，因此該文提出一種上下文信息融合與分支交互的SAR圖像艦船目標無錨框檢測方法，命名為CI-Net?？紤]到SAR圖中艦船尺度的多樣性，在特征提取階段設(shè)計上下文融合模塊，以自底向上的方式融合高低層信息，結(jié)合目標上下文信息，細化提取到的待檢測特征；其次，針對復(fù)雜場景中目標定位準確性不足的問題，提出分支交互模塊，在檢測階段利用分類分支優(yōu)化回歸分支的檢測框，改善目標定位框的精準性，同時將新增的IOU分支作用于分類分支，提高檢測網(wǎng)絡(luò)分類置信度，抑制低質(zhì)量的檢測框。實驗結(jié)果表明：在公開的SSDD和SAR-Ship-Dataset數(shù)據(jù)集上，該文方法均取得了較好的檢測效果，平均精度(AP)分別達到92.56%和88.32%，與其他SAR圖艦船檢測方法相比，該文方法不僅在精度上表現(xiàn)優(yōu)異，在摒棄了與錨框有關(guān)的復(fù)雜計算后，較快的檢測速度，對SAR圖像實時目標檢測也有一定的現(xiàn)實意義。
- 合成孔徑雷達 /
- 艦船檢測 /
- 無錨框 /
- 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數(shù)據(jù)集下載鏈接：https://zhuanlan.zhihu.com/p/143794468²⁾ SAR-Ship-Dataset數(shù)據(jù)集下載：https://pan.baidu.com/s/1PhSMkXVcuRM8M8xL15iBIQ

HTML全文

圖 1 無錨框的檢測模型

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圖 2 CI-Net檢測模型框架

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圖 3 上下文融合模塊

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圖 4 GCNet結(jié)構(gòu)

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圖 5 自注意力模塊

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圖 6 檢測結(jié)果對比圖

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圖 7 上下文融合模塊特征可視化

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圖 8 不同方法的P-R曲線圖

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表 1 艦船數(shù)據(jù)集的基本信息

數(shù)據(jù)集	傳感器來源	空間分辨率(m)	極化方式	輸入圖像大小	場景
SSDD	RadarSat-2, TerraSAR-X, Sentinel-1	1～15	VV, HH, VH, HV	500×500	近海、近岸區(qū)域
SAR-Ship Dataset	GF-3, Sentinel-1	3, 5, 8, 10等	VV, HH, VH, HV	256×256	遠海區(qū)域

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表 2 模型實驗結(jié)果

方法	上下文融合(CF)	分支交互(IB)	召回率(%)	準確率(%)	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：“×”表示沒有采用該模塊?！啊獭北硎静捎迷撃K。加粗值為每列最優(yōu)結(jié)果。

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表 3 不同方法在SSDD數(shù)據(jù)集上檢測性能對比

方法	單階段	無錨框	召回率(%)	準確率(%)	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

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表 4 不同方法在SAR-Ship-Dataset上檢測性能對比

方法	單階段	無錨框	召回率(%)	準確率(%)	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

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