一种形态学引导的解耦式SAR舰船有向检测框架

汪泽宇; 王青松

doi:10.11999/JEIT250979

一种形态学引导的解耦式SAR舰船有向检测框架

doi: 10.11999/JEIT250979 cstr: 32379.14.JEIT250979

汪泽宇,
王青松^,

中山大学电子与通信工程学院深圳 518107

基金项目: 国家自然科学基金(62273365)，“小米青年学者”项目

详细信息

作者简介:
汪泽宇：男，硕士生，研究方向为深度学习、目标检测、表征学习

王青松：男，教授，研究方向为遥感图像精化处理、协同探测感知与信息融合

通讯作者:
王青松　wangqs5@mail.sysu.edu.cn

中图分类号: TN; TP75; TP391.41
计量
- 文章访问数: 71
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- 被引次数: 0
出版历程
- 收稿日期: 2025-09-24
- 修回日期: 2025-12-10
- 录用日期: 2025-12-12
- 网络出版日期: 2025-12-25

A Morphology-guided Decoupled Framework for Oriented SAR Ship Detection

WANG Zeyu,
WANG Qingsong^,

School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds: The National Natural Science Foundation of China(62273365), Xiaomi Young Talents Program

摘要

摘要: 合成孔径雷达(SAR)以其全天时、全天候的观测能力，在遥感检测中得到了广泛应用。然而，受限于标注精度，目前主流的SAR目标检测方法多依赖水平框标注，难以实现精确的目标角度和尺度估计。同时，尽管弱监督学习在光学图像中的角度预测取得了进展，但其忽视了SAR特有的成像几何，难以有效泛化。为解决上述挑战，该文提出一种融合SAR成像机理的有向舰船检测新框架，核心思想在于将检测任务解耦为定位与方向估计两个独立的子模块。其中，定位模块可以直接利用任意现有的、在水平框标注上训练的检测器；而方向估计模块则在一个专门构建的形态学合成二值数据集上进行全监督训练。该框架的优势在于无需修改原有检测器结构和重新训练的前提下，即插即用地赋予模型高精度的有向框预测能力。实验验证了所提方法在多个数据集上相较于现有仅依赖水平框监督的方法表现出更优的性能，部分场景中甚至超越全监督方法，体现出强大的有效性与工程应用价值。
- 有向目标检测 /
- 形态学仿真 /
- 仿真驱动学习 /
- 解耦检测
Abstract: Objective Synthetic Aperture Radar (SAR) is an indispensable remote sensing technology; however, accurate ship detection in SAR imagery remains challenging. Most deep learning-based detection approaches rely on Horizontal Bounding Box (HBB) annotations, which do not provide sufficient geometric information to estimate ship orientation and scale. Although Oriented Bounding Box (OBB) annotation contains such information, reliable OBB labeling for SAR imagery is costly and frequently inaccurate because of speckle noise and geometric distortions intrinsic to SAR imaging. Weakly supervised object detection provides a potential alternative, yet approaches designed for optical imagery exhibit limited generalization capability in the SAR domain. To address these limitations, a simulation-driven decoupled framework is proposed. The objective is to enable standard HBB-based detectors to produce accurate OBB predictions without structural modification by training a dedicated orientation estimation module using a fully supervised synthetic dataset that captures essential SAR ship morphology. Methods The proposed framework decomposes oriented ship detection into two sequential sub-tasks: coarse localization and fine-grained orientation estimation (Fig. 1). First, an axis-aligned localization module based on a standard HBB detector, such as YOLOX, is trained using available HBB annotations to identify candidate regions of interest. This stage exploits the high-recall capability of mature detection networks and outputs image patches that potentially contain ship targets. Second, to learn orientation information without real OBB annotations, a large-scale morphological simulation dataset composed of binary images is constructed. The dataset generation begins with simple binary rectangles of randomized aspect ratios and known ground-truth orientations. To approximate the appearance of binarized SAR ship targets, morphological operations, including edge-level and region-level erosion and dilation, are applied to introduce boundary ambiguity. Structured strong scattering cross noise is further injected to simulate SAR-specific artifacts. This process yields a synthetic dataset with precise orientation labels. Third, an orientation estimation module based on a lightweight ResNet-18 architecture is trained exclusively on the synthetic dataset. This module predicts object orientation and refines aspect ratio using only shape and contour information. During inference, candidate patches produced by the localization module are binarized and processed by the orientation estimation module. Final OBBs are generated by fusing the spatial coordinates derived from the initial HBBs with the predicted orientation and refined dimensions. Results and Discussions The proposed method is evaluated on two public SAR ship detection benchmarks, HRSID and SSDD. Training is conducted using only HBB annotations, whereas performance is assessed against ground-truth OBBs using Average Precision at 0.5 intersection over union (AP50) and Recall (R). The method demonstrates superior performance relative to existing weakly supervised approaches and remains competitive with fully supervised methods (Table 1 and Table 2). On the HRSID dataset, an AP50 of 84.3% and a recall of 91.9% are achieved. These results exceed those of weakly supervised methods such as H2Rbox-v2 (56.2% AP50) and the approach reported by Yue et al.^[14] (81.5% AP50), and also outperform several fully supervised detectors, such as R-RetinaNet (72.7% AP50) and S2ANet (80.8% AP50). A similar advantage is observed on the SSDD dataset, where an AP50 of 89.4% is obtained, representing a significant improvement over the best reported weakly supervised result of 87.3%. Qualitative inspection of detection outputs supports these quantitative results (Fig. 3). The proposed method shows a lower missed-detection rate, particularly for small and densely clustered ships, relative to other weakly supervised approaches. This robustness is attributed to the high-recall property of the first-stage localization network combined with reliable orientation cues learned from the morphological dataset. To examine key methodological aspects, additional experiments are conducted. Analysis of the domain gap between synthetic and real data using UMAP-based visualization of high-dimensional features (Fig. 5) reveals substantial overlap and similar manifold structures across domains, indicating strong morphological consistency. An ablation study of the morphological components (Fig. 4) further shows that each simulation element contributes incrementally to performance improvement, supporting the design of the high-fidelity simulation process. Conclusions A morphology-guided decoupled framework for oriented ship detection in SAR imagery is presented. By separating localization and orientation estimation, standard HBB-based detectors are enabled to perform accurate oriented detection without retraining. The central contribution is a fully supervised morphological simulation dataset that allows a dedicated module to learn robust orientation features from structural contours, thereby mitigating the annotation challenges associated with real SAR data. Experimental results demonstrate that the proposed approach substantially outperforms existing HBB-supervised methods and remains competitive with fully supervised alternatives. The plug-and-play design highlights its practical applicability.
- Oriented objective detection /
- Morphological simulation /
- Simulation-driven learning /
- Decoupled detection

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图 1 模型总览图

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图 2 不同仿真条件单独作用时的效果图

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图 3 不同方法的检测可视化结果对比图

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图 4 仿真数据集样本多样性结果展示

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图 5 不同源的数据分布降维可视化

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图 6 不同方法使用不同训练样本量对AP50精度的影响

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表 1 本文方法与典型检测方法在HRSID数据集上的性能比较(%)

方法	监督方式	AP50	R
R-RetinaNet^[18]	有向框监督	72.7	79.2
GWD^[19]	有向框监督	73.3	79.1
S2ANet^[20]	有向框监督	80.8	83.9
R-Faster-RCNN^[3]	有向框监督	77.3	82.1
RoI Transformer^[21]	有向框监督	83.8	86.4
H2Rbox-v2^[7]	水平框监督	56.2	68.6
Wholly-WOOD^[8]	水平框监督	61.5	64.5
文献[14]	水平框监督	81.5	85.0
本文方法	水平框监督	84.3	91.9

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表 2 本文方法与典型检测方法在SSDD数据集上的性能比较(%)

方法	监督方式	AP50	R
R-RetinaNet^[18]	有向框监督	79.9	84.0
GWD^[19]	有向框监督	82.5	85.9
S2ANet^[20]	有向框监督	88.4	90.0
R-Faster-RCNN^[3]	有向框监督	87.7	89.8
RoI Transformer^[21]	有向框监督	89.5	91.0
文献[14]	水平框监督	87.3	89.7
本文方法	水平框监督	89.4	95.6

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表 3 不同轴对齐检测器对最终精度的影响(%)

数据集	轴对齐检测器	HBBs AP50	OBBs AP50	HBBs R	OBBs R
SSDD	FCOS^[22]	81.9	78.1	91.2	88.8
	CenterNet^[23]	90.2	88.5	96.7	94.7
	Faster-RCNN^[3]	90.0	88.1	93.0	90.1
	YOLOX^[4]	90.3	89.4	99.1	95.6
HRSID	FOCS^[22]	78.4	71.2	87.6	80.3
	CenterNet^[23]	86.6	77.5	92.3	85.1
	Faster-RCNN^[3]	79.7	70.3	83.5	78.7
	YOLOX^[4]	89.0	84.3	96.1	90.8

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参考文献(24)

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