SCTD 3.0构建了真实海况下的大规模声呐目标检测数据集，助力水下目标识别。
SCTD 3.0: Sonar Common Target Detection in the Wild - A Large-Scale, Multi-Scene Dataset from Real Marine Surveys
- 基于多频实测声呐图像，覆盖10类目标与多种海底地貌。
- 超1万张高质量图像，支持跨域、跨场景、跨频率的模型评估。
- 提供细粒度标注，适合水下探测与智能感知研究者使用。
合成孔径声呐（SAS）是广域小目标水下探测的核心技术。然而，大规模、高质量的SAS数据集稀缺，制约了数据驱动的识别发展。现有基准规模小且局限于单一场景，难以还原真实探测中的复杂声学散射、多样海底形态及多姿态成像。为填补这一空白，我们提出SCTD 3.0——一个面向自然水域中常见水下目标检测的真实测量大型数据集。该数据集包含超过10,000张来自多频系统（240 kHz、450 kHz等）的高质量实测SAS图像片段，涵盖十类典型目标，覆盖不同海底地形、观测角度、探测距离与频率范围。我们建立了分层标注协议，分离标注目标的物理属性、部署特征与散射现象，包括材料、几何结构、内部构造、埋藏状态、阴影完整性、镜面反射、边缘衍射与共振效应，实现细粒度目标刻画。同时构建了多任务评测基准，涵盖目标检测、细粒度分类与属性预测，评估主流深度学习模型在跨域、跨场景、跨频率与跨视角条件下的泛化能力。SCTD 3.0有望成为开放水域下稳健水下目标感知的关键数据基石。数据集已开源：https://github.com/automlresearch/SCTD-3.0。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。