AI 中文总结
该研究推出SCTD 3.0数据集，解决了现有SAS数据集规模小、场景单一的问题，构建多任务基准评估深度学习模型，为水下目标感知提供关键数据支撑。
AI 中文摘要
合成孔径声呐（SAS）是大范围检测小型水下目标的核心技术。然而，大规模、高质量的SAS数据集稀缺，阻碍了数据驱动的识别研究。现有基准数据集规模小且局限于单一场景，无法复现真实检测中的复杂声学散射、多样海底及多姿态成像。为填补这一空白，我们推出SCTD 3.0——用于野外声呐共同目标检测的大规模实测数据集，采集自天然水域。该数据集包含10000余张来自多频系统（240 kHz、450 kHz及其他频率）的高质量真实SAS图像片段，涵盖十种典型目标类别，涉及多样海底地貌，包含多种观测角度、探测距离和频段。我们建立了严格的分层标注协议，将固有物理属性、部署特征及散射现象的标注解耦，涵盖材料、几何形状、内部结构、埋藏状态、阴影完整性、镜面反射、边缘衍射及共振效应，实现了细粒度的目标表征。我们还构建了用于目标检测、细粒度分类及属性预测的多任务基准，在跨域、跨场景、跨频率和跨视角泛化场景下评估主流深度学习模型。SCTD 3.0有望为开放水域环境中鲁棒的水下目标感知提供关键数据基石，其相关资源可通过指定URL获取。
英文摘要
Synthetic Aperture Sonar (SAS) is core for wide-area detection of small underwater targets. However, large-scale, high-quality SAS datasets are scarce, hindering data-driven recognition. Existing benchmarks are small and limited to single scenarios, failing to reproduce complex acoustic scattering, diverse seabeds, and multi-pose imaging in real detection. To fill this gap, we introduce SCTD 3.0 - a large-scale real-measured dataset for Sonar Common Target Detection in the Wild in natural waters. It contains over 10,000 high-quality real SAS image snippets from multi-frequency systems (240 kHz, 450 kHz, and others), covering ten typical target categories across varied seabed geomorphologies, with multiple observation angles, detection ranges, and frequency bands. We establish a rigorous hierarchical annotation protocol that decouples labeling of intrinsic physical properties, deployment characteristics, and scattering phenomena - covering material, geometry, internal structure, burial state, shadow integrity, specular highlights, edge diffraction, and resonance effects. This enables fine-grained target characterization. We also construct a multi-task benchmark for object detection, fine-grained classification, and attribute prediction, evaluating mainstream deep learning models under cross-domain, cross-scene, cross-frequency, and cross-view generalization. SCTD 3.0 is expected to provide a critical data cornerstone for robust underwater target perception in open-water environments. SCTD 3.0 is available at https://github.com/automlresearch/SCTD-3.0.
