面向复杂海洋环境的侧扫声呐目标样本生成与增强方法

doi:10.11947/j.AGCS.2026.20250503

摘要/Abstract

摘要：

针对水下目标检测中实测样本获取困难、传统数据增强方法难以有效表征侧扫声呐目标散射特性的问题，本文提出了一种基于侧扫声呐成像机理的沉船目标样本生成方法。基于声能方程与射线传播模型，构建了顾及回波散射强度特征的沉船目标声学成像模型；然后，考虑海底地形地貌对声学成像的影响，基于风格迁移方法对生成图像进行增强，以提高目标图像的真实性与环境一致性；最后，结合环境噪声模型，对目标图像进行噪声扰动与环境特征增强，生成多场景沉船目标样本数据。试验结果表明，仅利用生成样本训练的检测模型取得了0.88 mAP（0.5）的检测精度，验证了本文方法在少样本水下目标检测任务中的有效性，能够为复杂海洋环境下侧扫声呐目标检测模型的训练提供数据支撑。

关键词: 侧扫声呐, 样本扩增, 风格迁移, 水下目标检测, 深度学习

Abstract:

To address the difficulty of acquiring real side-scan sonar (SSS) target samples and the limited capability of conventional augmentation methods in representing acoustic scattering characteristics, this study proposes a target sample generation framework based on the SSS imaging mechanism. An underwater target imaging model integrating echo intensity and acoustic ray propagation is first constructed according to the SSS imaging principle and acoustic energy propagation model. Subsequently, considering the influence of seabed topography and geomorphology on acoustic imaging, an improved style transfer strategy is introduced to enhance the generated images and improve their realism and environmental consistency. In addition, an environmental noise model is incorporated to perform noise perturbation and environmental feature enhancement, thereby generating multi-scene shipwreck target samples. Experimental results demonstrate that the detection model trained solely on the generated samples achieves a detection accuracy of 0.88 mAP (0.5), verifying the effectiveness of the proposed sample generation method for few-shot underwater target detection tasks. The proposed method can provide data support for training side-scan sonar target detection models in complex marine environments.

Key words: side-scan sonar, sample amplification, style transfer, underwater object detection, deep learning

中图分类号:

P228

赵曦, 袁强强, 徐佳丹. 面向复杂海洋环境的侧扫声呐目标样本生成与增强方法[J]. 测绘学报, 2026, 55(7): 1229-1239.

Xi Zhao, Qiangqiang Yuan, Jiadan Xu. A sample generation and enhancement method for side-scan sonar targets in complex marine environments[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(7): 1229-1239.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250503

http://xb.chinasmp.com/CN/Y2026/V55/I7/1229

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数据集类型	样本	训练集	数据集样本量
测试集	实测SSS样本	Te	101
训练集	实测SSS样本	Tr1	328
	基于Tr1进行旋转、缩放操作扩增后的SSS样本	Tr1^*	854
	原始仿真SSS样本	Tr2	427
	仿真目标与背景叠加后样本	Tr3	854
	Tr3基础上加噪后的仿真SSS样本	Tr4	854
	Tr3基础上风格迁移后的仿真SSS样本	Tr5	854
	Tr3基础上先加噪后风格迁移的仿真样本	Tr6	854
	Tr3基础上先风格迁移后加噪的仿真样本	Tr7	854
	部分Tr7仿真样本（526张）与Tr1实测样本（328张）合集	Tr7^*	854
	全部仿真样本（Tr2—Tr7）	Tr8	4697

模型	训练集	P	R	mAP（0.5）	mAP（0.5～0.95）	平均推理时间/（ms/frame）
1	Tr1	0.870	0.879	0.893	0.636	9.382
1^*	Tr1^*	0.931	0.876	0.917	0.646	9.412
2	Tr2	0.537	0.664	0.576	0.301	9.523
3	Tr3	0.719	0.542	0.642	0.373	9.471
4	Tr4	0.811	0.8	0.805	0.486	9.44
5	Tr5	0.748	0.738	0.757	0.434	9.493
6	Tr6	0.779	0.759	0.774	0.531	9.434
7	Tr7	0.879	0.869	0.880	0.623	9.467
7^*	Tr7^*	0.890	0.881	0.902	0.640	9.501
8	Tr8	0.908	0.888	0.909	0.643	9.655

模型	真实样本数	样本数仿真	总数	仿真样本比例/（%）	mAP（0.5）
Mix-A	554	300	854	35.1	0.892
Mix-B（基线模型）	328	526	854	61.6	0.902
Mix-C	154	700	854	82.0	0.910