用物理仿真生成逼真声呐图像，并定量验证与真实数据的差异。
Physics-informed simulation framework for realistic sonar image generation and statistical validation
- 基于物理引擎控制海底纹理、光照阴影等参数生成声呐图。
- 纹理匹配度高（KL < 0.07），平面类图像比船类更接近真实。
- 无需生成模型，适合做水下图像数据集的可信评估。
合成声呐数据集为昂贵的真实采集提供了可扩展的替代方案，但其有效性受限于缺乏严格的量化验证。本文提出ACOUSIM（ACOustic SIMulation and Validation Platform），一个基于物理信息的框架，无需依赖生成模型即可评估合成与真实声呐图像间的统计一致性。该框架基于Gazebo环境，通过显式控制海底纹理、光照驱动的阴影、平台高度和噪声生成类声呐图像。真实感在两个公开数据集SeabedObjects-KLSG-II和Sonar Common Target Detection（SCTD）上进行量化评估，采用全局强度与局部纹理（LBP）分布，通过Kullback-Leibler散度、Jensen-Shannon散度和Earth Mover's Distance进行分析。结果表明所有类别纹理对齐良好（KL < 0.07），平面类强度匹配优于船类，因阴影几何结构更复杂。ACOUSIM建立了一个可复现的分布级基准，直接支持水下图像分析中数据集的可靠验证。
原文摘要 · Abstract (English)
Synthetic sonar datasets offer a scalable alternative to costly real-world acquisition, yet their utility remains limited by the absence of rigorous quantitative validation. We present ACOUSIM (ACOustic SIMulation and Validation Platform), a physics-informed framework that evaluates the statistical alignment between synthetic and real sonar imagery without relying on generative models. A Gazebo-based environment generates sonar-like images by explicitly controlling seabed texture, illumination-driven shadowing, platform altitude, and noise. Realism is quantified against two public sonar datasets, SeabedObjects-KLSG-II and Sonar Common Target Detection (SCTD), using global intensity and local texture (LBP) distributions assessed via Kullback-Leibler divergence, Jensen-Shannon divergence, and Earth Mover's Distance. Results show strong texture alignment (KL < 0.07) across all classes, with plane-class intensity alignment outperforming ship-class due to shadow geometry complexity. ACOUSIM establishes a reproducible, distribution-level baseline for sim-to-real sonar evaluation and directly supports reliable dataset validation for underwater image analysis.
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