基于YOLO模型合并的SAR图像人造目标检测

何适存; 王欣超; 陈思伟

doi:10.12000/JR26080

基于YOLO模型合并的SAR图像人造目标检测

DOI: 10.12000/JR26080 CSTR: 32380.14.JR26080

国防科技大学电子科学学院长沙 410073

基金项目: 国家自然科学基金(U24B20189, 62122091)，湖南省省级科技计划(2024RC1040)

详细信息

作者简介:
何适存，硕士生，主要研究方向为深度学习、SAR图像目标检测识别

王欣超，博士生，主要研究方向为深度学习、遥感基础模型

陈思伟，教授，主要研究方向为极化雷达成像、目标识别、机器学习、电子对抗等

通讯作者:
陈思伟 chenswnudt@163.com

责任主编：高贵 Corresponding Editor: GAO Gui

中图分类号: TN958
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出版历程
- 收稿日期: 2026-04-29
- 修回日期: 2026-07-05
- 网络出版日期: 2026-07-27

SAR Image Artificial Target Detection Based on YOLO Model Merging

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (U24B20189, 62122091), The Science and Technology Innovation Program of Hunan Province (2024RC1040)

More Information

Corresponding author: CHEN Siwei, chenswnudt@163.com

摘要

摘要: 高价值人造目标检测是合成孔径雷达(SAR)的重要应用。现有基于深度学习的检测方法，对模型中的参数化知识的有效复用不足，在不同数据集上常表现出泛化性能弱的问题，导致实际迁移应用困难。针对这一问题，该文从向模型学习(LFM)的角度出发，提出一种基于YOLO模型合并的SAR图像人造目标检测方法。该方法的核心思想是以多个同构模型为知识来源，将源模型中已形成的特征提取、多尺度特征融合和目标判别能力整合到统一检测框架中，实现已有模型知识向检测性能增益的转化。该方法以共享预训练骨干网络为基础，首先将多源模型的颈部网络参数迁移至合并模型。随后，采用多尺度通道拼接并结合点卷积的方式，实现多源模型的多尺度特征融合。同时，在各特征尺度并行拓展检测头分支，以保持每个源模型的判别能力。基于SADD, SSDD和HRSID 3个公开SAR数据集的实验结果表明，所提方法可有效提升SAR图像飞机、舰船等人造目标的检测性能。在Recall, mAP50和mAP50-95指标上均实现了稳定的性能增益，尤其在高交并比阈值和少样本场景下，展现出更优的检测鲁棒性与泛化能力。
- 合成孔径雷达 /
- 人造目标检测 /
- 模型合并 /
- 特征融合 /
- 参数化知识
Abstract: High-value artificial target detection is a critical application of Synthetic Aperture Radar (SAR). Existing deep learning-based detection methods often fail to adequately reuse parameterized knowledge from trained models and demonstrate limited generalization across different datasets. This limitation hinders their practical application in new scenarios. To address this issue, this study proposes a SAR image artificial target detection method based on YOLO model merging, adopting a Learning From Models (LFM) approach. The core idea is to use multiple homogeneous models as knowledge sources. The method integrates the feature extraction, multiscale feature fusion, and target discrimination capabilities developed in these source models into a unified detection framework, thereby transforming existing model knowledge into improved detection performance. Specifically, based on a shared pretrained backbone, the neck network parameters of multiple source models are first transferred to the merged model. Multiscale feature fusion is then achieved through channel concatenation and point-wise convolution. In addition, parallel detection head branches are introduced at each feature scale to preserve the discriminative capabilities of the source models. Experimental results on three public SAR datasets (SADD, SSDD, and HRSID) demonstrate that the proposed method effectively improves the detection performance of man-made targets in SAR images. Consistent improvements are observed in Recall, mAP50, and mAP50-95, particularly under high intersection-over-union thresholds and limited-sample scenarios, where the proposed method exhibits superior robustness and generalization ability.
- Synthetic aperture radar (SAR) /
- Artificial target detection /
- Model merging /
- Feature fusion /
- Parameterized knowledge

HTML全文

图 1 基于YOLO模型合并的SAR图像人造目标检测方法框图

Figure 1. Block diagram of SAR image artificial target detection method based on YOLO model merging

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图 2 源模型网络结构

Figure 2. Network architecture of the source model

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图 3 设计的合并模型网络结构

Figure 3. Network architecture of the designed merged model

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图 4 SADD目标检测实验数据集划分方式

Figure 4. Dataset partitioning strategy for SADD target detection experiments

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图 5 源模型、集成学习方法与合并模型在SADD数据集上的检测结果对比

Figure 5. Comparison of detection results among source models, ensemble learning methods, and the merged model on the SADD dataset

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图 6 不同比例训练-测试集下的性能差值柱状图(相对源模型)

Figure 6. Bar chart of performance differences under different train-test split ratios (relative to the source model)

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图 7 源模型与合并模型在SSDD数据集上的检测结果对比

Figure 7. Comparison of detection results between the source models and the merged model on the SSDD dataset

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图 8 源模型与合并模型在HRSID数据集上的检测结果对比

Figure 8. Comparison of detection results between the source models and the merged model on the HRSID dataset

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图 9 SSDD-HRSID异源实验中真值标注与融合前后特征响应对比

Figure 9. Comparison of ground-truth annotations and feature responses before and after fusion in the SSDD-HRSID heterogeneous experiment

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表 1 目标检测模型参数组成

Table 1. Parameter composition of target detection models

参数类型	参数命名	可学习参数
卷积权重	$* $.conv.weight	是
BN缩放	$* $.bn.weight	是
BN平移	$* $.bn.bias	是
其他权重	$* $.weight	是
其他偏置	$* $.bias	是
BN缓冲区	$* $.bn.running_mean	否
	$* $.bn.running_var	否
	$* $.bn.num_batches_tracked	否
注：$* $.指参数所属的具体模块。

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表 2 实验数据集数量分布

Table 2. Number distribution of experimental datasets

目标类型	数据集	训练样本数(张)	验证样本数(张)	平均目标数(个/张)
飞机	SADD	2373	593	2.64
舰船	SSDD	928	232	2.12
舰船	HRSID	3643	1961	3.02

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表 3 源模型、集成学习方法及合并模型在SADD数据集上的检测结果

Table 3. Detection results of source models, ensemble learning methods, and the merged model on the SADD dataset

模型版本	方法	Precision	Recall	mAP50	mAP50-95
v5	avg(基线)	0.933	0.900	0.954	0.576
	NMS集成	0.928	0.922	0.967	0.592
	Voting	0.869	0.932	0.946	0.567
	WBF	0.875	0.931	0.947	0.570
	Merging	0.918	0.939	0.968	0.593
v8	avg(基线)	0.925	0.902	0.955	0.609
	NMS集成	0.953	0.901	0.960	0.656
	Voting	0.872	0.947	0.958	0.661
	WBF	0.879	0.953	0.957	0.663
	Merging	0.938	0.955	0.971	0.688
v11	avg(基线)	0.937	0.895	0.953	0.604
	NMS集成	0.892	0.938	0.960	0.630
	Voting	0.885	0.949	0.958	0.638
	WBF	0.884	0.945	0.957	0.637
	Merging	0.950	0.906	0.968	0.656
注：表中加粗数值表示最优结果。

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表 4 模型参数量、运算量与推理效率统计

Table 4. Model parameter, computational cost, inference efficiency

模型版本	方法	Params (M)	$\Delta $Params (M)	FLOPs (G)	$\Delta $FLOPs (G)	FPS (fps)
v5	avg(基线)	20.8	—	47.9	—	122.1
	NMS集成	41.6	+20.8	95.8	+47.9	42.6
	Voting	41.6	+20.8	95.8	+47.9	41.5
	WBF	41.6	+20.8	95.8	+47.9	41.5
	Merging	29.5	+8.7	64.7	+16.8	83.9
v8	avg(基线)	23.2	—	67.4	—	91.2
	NMS集成	46.4	+23.2	134.8	+67.4	25.1
	Voting	46.4	+23.2	134.8	+67.4	24.9
	WBF	46.4	+23.2	134.8	+67.4	29.0
	Merging	34.6	+11.4	96.4	+29.0	63.9
v11	avg(基线)	20.0	—	67.6	—	76.4
	NMS集成	40.0	+20.0	135.2	+67.6	26.3
	Voting	40.0	+20.0	135.2	+67.6	31.6
	WBF	40.0	+20.0	135.2	+67.6	25.7
	Merging	29.7	+9.7	96.0	+28.4	53.2

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表 5 SSDD与HRSID数据集交叉验证实验

Table 5. Cross-validation experiment between SSDD and HRSID datasets

模型版本	模型来源	SSDD-test				HRSID-test
模型版本	模型来源	Precision	Recall	mAP50	mAP50-95	Precision	Recall	mAP50	mAP50-95
v5	SSDD	0.968	0.907	0.978	0.734	0.838	0.575	0.706	0.481
	HRSID	0.720	0.560	0.640	0.353	0.899	0.794	0.891	0.634
	Merge	0.930	0.951	0.983	0.721	0.901	0.810	0.902	0.662
v8	SSDD	0.942	0.929	0.977	0.705	0.844	0.573	0.699	0.444
	HRSID	0.795	0.570	0.697	0.397	0.905	0.773	0.882	0.615
	Merge	0.957	0.949	0.981	0.736	0.915	0.822	0.913	0.679
v11	SSDD	0.959	0.912	0.976	0.730	0.811	0.575	0.711	0.482
	HRSID	0.819	0.547	0.672	0.365	0.904	0.802	0.899	0.655
	Merge	0.929	0.951	0.982	0.718	0.908	0.809	0.901	0.670
注：表中加粗数值表示最优结果。

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表 6 代表性检测方法全量数据对比实验

Table 6. Full-data comparison of representative detection methods

方法	SSDD-test				HRSID-test
方法	Precision	Recall	mAP50	mAP50-95	Precision	Recall	mAP50	mAP50-95
FCOS	0.843	0.923	0.886	0.598	0.843	0.816	0.781	0.573
Faster R-CNN	0.890	0.938	0.955	0.684	0.794	0.833	0.866	0.614
Swin-Transformer	0.889	0.927	0.956	0.680	0.843	0.831	0.850	0.621
YOLOv8 (Union)	0.927	0.894	0.954	0.706	0.895	0.766	0.881	0.676
YOLOv8 (Merge)	0.957	0.949	0.981	0.736	0.915	0.822	0.913	0.679
注：表中加粗数值表示最优结果。

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表 7 代表性检测方法计算复杂度与参数量对比

Table 7. Comparison of FLOPs and Params among representative detection methods

方法	FLOPs (G)	Params (M)
FCOS	88.6	32.1
Faster R-CNN	112.8	41.4
Swin-Transformer	94.3	27.5
YOLOv8 (Union)	67.4	23.2
YOLOv8 (Merge)	96.4	34.6

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