ONCship: A Large-Scale Underwater Acoustic Corpus for Ship Recognition
Recent advances in deep learning have significantly improved underwater acoustic vessel recognition. However, existing publicly available datasets are typically limited in scale, hindering large-scale representation learning and downstream task evaluation. To address this limitation, we introduce ONCship, a large-scale underwater acoustic dataset comprising 4521 hours of curated recordings from Ocean Networks Canada (ONC) hydrophones and Automatic Identification System (AIS) data. We evaluate the proposed dataset across three tasks: self-supervised representation learning, vessel-type classification, and MMSI-Level Vessel Classification. Experiments on DeepShip and ShipsEar demonstrate th...
| Authors | Zhe Cao, Jichao Zhang, Xiao-Lei Zhang |
|---|---|
| Primary category | cs.SD |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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