SCTD 3.0: Sonar Common Target Detection in the Wild - A Large-Scale, Multi-Scene Dataset from Real Marine Surveys

• URL: http://arxiv.org/abs/2608.08106v1
• Date: Sat, 08 Aug 2026 12:39:22 GMT
• Title: SCTD 3.0: Sonar Common Target Detection in the Wild - A Large-Scale, Multi-Scene Dataset from Real Marine Surveys
• Abstract summary: SCTD 3.0 is a large-scale real-measured dataset for Sonar Common Target Detection in the Wild in natural waters.<n>It contains over 10,000 high-quality real SAS image snippets from multi-frequency systems.<n>It is expected to provide a critical data cornerstone for robust underwater target perception in open-water environments.
• Score: 3.272585676511349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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