Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition

• URL: http://arxiv.org/abs/2401.02099v3
• Date: Tue, 11 Jun 2024 02:33:56 GMT
• Title: Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition
• Abstract summary: We propose Oceanship, a large-scale and diverse underwater audio dataset. This dataset comprises 15 categories, spans a total duration of 121 hours, and includes comprehensive annotation information such as coordinates, velocity, vessel types, and timestamps. We introduce a baseline model named Oceannet for underwater audio retrieval. This model achieves a recall at 1 (R@1) accuracy of 67.11% and a recall at 5 (R@5) accuracy of 99.13% on the Deepship dataset.
• Score: 22.890584326036976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recognition of underwater audio plays a significant role in identifying a vessel while it is in motion. Underwater target recognition tasks have a wide range of applications in areas such as marine environmental protection, detection of ship radiated noise, underwater noise control, and coastal vessel dispatch. The traditional UATR task involves training a network to extract features from audio data and predict the vessel type. The current UATR dataset exhibits shortcomings in both duration and sample quantity. In this paper, we propose Oceanship, a large-scale and diverse underwater audio dataset. This dataset comprises 15 categories, spans a total duration of 121 hours, and includes comprehensive annotation information such as coordinates, velocity, vessel types, and timestamps. We compiled the dataset by crawling and organizing original communication data from the Ocean Communication Network (ONC) database between 2021 and 2022. While audio retrieval tasks are well-established in general audio classification, they have not been explored in the context of underwater audio recognition. Leveraging the Oceanship dataset, we introduce a baseline model named Oceannet for underwater audio retrieval. This model achieves a recall at 1 (R@1) accuracy of 67.11% and a recall at 5 (R@5) accuracy of 99.13% on the Deepship dataset.

Related papers

• UniqueShip: Mitigating Data Leakage in Acoustic Ship Classification Benchmark Datasets [3.8240667211978163]
  We introduce UniqueShip, a machine learning-ready benchmark dataset for Underwater Acoustic Target Recognition.<n>Unlike previous datasets, we explicitly control for "data leakage" between the training and evaluation sets.<n>UniqueShip contains 2,460 hours of ship-radiated audio from 4,218 unique vessels.
  arXiv  Detail & Related papers  (2026-09-12T02:31:38Z)
• Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings [0.003557754373323816]
  Ocean-bottom seismometers (OBS) record low-frequency sound for months to years across broad areas of ocean.<n>We present a deep learning semantic segmentation framework that detects the 20-Hz notes of fin whales in OBS spectrograms.<n>Applying to 378,912 h of recordings from 46 OBS sites, the detector identified 6.3 million calls, forming the largest fin whale call catalogue assembled to date.
  arXiv  Detail & Related papers  (2026-09-08T11:43:41Z)
• WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects [68.44670476527155]
  WUTDet is a large-scale ship detection dataset with 100,576 images and 381,378 annotated ship instances.<n>We systematically evaluate 20 baseline models from three mainstream detection architectures, namely CNN, Transformer, and Mamba.<n>Results on Ship-GEN show that models trained on WUTDet exhibit stronger generalization under different data distributions.
  arXiv  Detail & Related papers  (2026-04-09T03:28:05Z)
• NASTaR: NovaSAR Automated Ship Target Recognition Dataset [1.8957631297817699]
  This dataset comprises of 3415 ship patches extracted from NovaSAR S-band imagery.<n>It includes distinctive features such as 23 unique classes, inshore/offshore separation, and an auxiliary wake dataset for patches where ship wakes are visible.<n>We validated the dataset applicability across prominent ship-type classification scenarios using benchmark deep learning models.
  arXiv  Detail & Related papers  (2025-12-20T20:42:30Z)
• NAUTILUS: A Large Multimodal Model for Underwater Scene Understanding [60.76337064425815]
  We study the underwater scene understanding methods, which aim to achieve automated underwater exploration.<n>NautData is a dataset containing 1.45 M image-text pairs supporting eight underwater scene understanding tasks.<n>We propose a plug-and-play vision feature enhancement (VFE) module, which explicitly restores clear underwater information.
  arXiv  Detail & Related papers  (2025-10-31T14:00:35Z)
• The iNaturalist Sounds Dataset [60.157076990024606]
  iNatSounds is a collection of 230,000 audio files capturing sounds from over 5,500 species, contributed by more than 27,000 recordists worldwide.<n>The dataset encompasses sounds from birds, mammals, insects, reptiles, and amphibians, with audio and species labels derived from observations submitted to iNaturalist.<n>We envision models trained on this data powering next-generation public engagement applications, and assisting biologists, ecologists, and land use managers in processing large audio collections.
  arXiv  Detail & Related papers  (2025-05-31T02:07:37Z)
• Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation [67.23953699167274]
  Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO) In EO, this challenge is amplified by the redundancy and heavy-tailed distributions common in satellite imagery. We propose a dynamic dataset pruning strategy designed to improve SSL pre-training by maximizing dataset diversity and balance.
  arXiv  Detail & Related papers  (2025-04-09T15:13:26Z)
• The Marine Debris Forward-Looking Sonar Datasets [10.878811189489804]
  This paper presents the Marine Debris Forward-Looking Sonar datasets, with three different settings. We provide full dataset description, basic analysis and initial results for some tasks. We expect the research community will benefit from this dataset.
  arXiv  Detail & Related papers  (2025-03-28T21:12:03Z)
• Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark [65.79402756995084]
  Real Acoustic Fields (RAF) is a new dataset that captures real acoustic room data from multiple modalities. RAF is the first dataset to provide densely captured room acoustic data.
  arXiv  Detail & Related papers  (2024-03-27T17:59:56Z)
• WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database [49.1574468325115]
  We introduce textbfWhaleNet (Wavelet Highly Adaptive Learning Ensemble Network), a sophisticated deep ensemble architecture for the classification of marine mammal vocalizations. We achieve an improvement in classification accuracy by $8-10%$ over existing architectures, corresponding to a classification accuracy of $97.61%$.
  arXiv  Detail & Related papers  (2024-02-20T11:36:23Z)
• Leveraging Citizen Science for Flood Extent Detection using Machine Learning Benchmark Dataset [0.9029386959445269]
  We create a labeled known water body extent and flooded area extents during known flooding events covering about 36,000 sq. kilometers of regions within mainland U.S and Bangladesh. We also leveraged citizen science by open-sourcing the dataset and hosting an open competition based on the dataset to rapidly prototype flood extent detection using community generated models. We believe the dataset adds to already existing datasets based on Sentinel-1C SAR data and leads to more robust modeling of flood extents.
  arXiv  Detail & Related papers  (2023-11-15T18:49:29Z)
• The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft [0.0]
  This paper introduces AeroSonicDB (YPAD-0523), a dataset of low-flying aircraft sounds for training acoustic detection and classification systems. This paper describes the method of exploiting ADS-B radio transmissions to passively collect and label audio samples. The dataset contains 625 aircraft recordings ranging in event duration from 18 to 60 seconds, for a total of 8.87 hours of aircraft audio.
  arXiv  Detail & Related papers  (2023-11-10T19:41:10Z)
• Histogram Layer Time Delay Neural Networks for Passive Sonar Classification [58.720142291102135]
  A novel method combines a time delay neural network and histogram layer to incorporate statistical contexts for improved feature learning and underwater acoustic target classification. The proposed method outperforms the baseline model, demonstrating the utility in incorporating statistical contexts for passive sonar target recognition.
  arXiv  Detail & Related papers  (2023-07-25T19:47:26Z)
• Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles [63.20765930558542]
  3D semantic data are useful for core perception tasks such as obstacle detection and ego-vehicle localization. We propose a new dataset, Navya 3D (Navya3DSeg), with a diverse label space corresponding to a large scale production grade operational domain. It contains 23 labeled sequences and 25 supplementary sequences without labels, designed to explore self-supervised and semi-supervised semantic segmentation benchmarks on point clouds.
  arXiv  Detail & Related papers  (2023-02-16T13:41:19Z)
• A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection [0.0]
  Multibeam forward-looking sonar (MFLS) plays an important role in underwater detection. There are several challenges to the research on underwater object detection with MFLS. We present a novel dataset, consisting of over 9000 MFLS images captured using Tritech Gemini 1200ik sonar.
  arXiv  Detail & Related papers  (2022-12-01T08:26:03Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Deep Learning for Surface Wave Identification in Distributed Acoustic Sensing Data [1.7237878022600697]
  We present a highly scalable and efficient approach to process real, complex DAS data. Deep supervised learning is used to identify "useful" coherent surface waves generated by anthropogenic activity. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.
  arXiv  Detail & Related papers  (2020-10-15T15:53:03Z)
• Real-Time target detection in maritime scenarios based on YOLOv3 model [65.35132992156942]
  A novel ships dataset is proposed consisting of more than 56k images of marine vessels collected by means of web-scraping. A YOLOv3 single-stage detector based on Keras API is built on top of this dataset.
  arXiv  Detail & Related papers  (2020-02-10T15:25:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.