UniqueShip: Mitigating Data Leakage in Acoustic Ship Classification Benchmark Datasets
Underwater Acoustic Target Recognition (UATR) of ships is well-suited for machine learning, yet its progress is hindered by the lack of large, diverse, and publicly available labeled datasets. In this work, we introduce UniqueShip, a machine learning-ready benchmark dataset for UATR applications sourced from the open Ocean Networks Canada (ONC) repository. Unlike previous datasets, we explicitly control for "data leakage" between the training and evaluation sets to ensure more reliable and generalizable model evaluation that does not encourage the model to memorize individual ships. We demonstrate that typical, random data partitioning in two prominent UATR datasets leads to falsely optim...
| Authors | Connor Hashemi, Trevor Stout, Anthony Hoogs et al. |
|---|---|
| Primary category | cs.SD |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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