New dataset SCTD 3.0 aims to advance underwater target detection

Researchers have introduced SCTD 3.0, a large-scale dataset designed to improve the detection of underwater targets using Synthetic Aperture Sonar (SAS). This dataset features over 10,000 real SAS image snippets across ten target categories, captured with multi-frequency systems in natural waters. SCTD 3.0 includes a rigorous annotation protocol for fine-grained target characterization and establishes a multi-task benchmark to evaluate deep learning models on generalization across various conditions. AI

IMPACT Aims to improve underwater target perception by providing a robust dataset for training and evaluating deep learning models.

RANK_REASON The cluster describes a new dataset and benchmark for a specific computer vision task, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

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• deep learning
• SCTD 3.0
• Sonar Common Target Detection in the Wild
• synthetic aperture sonar

• paper
• other

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