The PNNL-TUNAMELT Dataset for Automated Detection Around Marine Energy Devices
This labeled dataset contains 107,451 acoustic camera video frames capturing marine life interactions around an underwater tidal turbine. Each frame is annotated with bounding boxes identifying marine life objects as labeled by a fish biologist. Created to support research into automated target detection around underwater turbines, this dataset aims to advance capabilities that enable the safe deployment and operation of marine energy devices. No collisions were observed with the turbine while labeling and analyzing this dataset, and a publication detailing this work will be added once available.
The video data was originally collected in 2010 around Ocean Renewable Power Company's (ORPC) tidal turbine deployment in Cobscook Bay, Maine, USA, with results published in Viehman and Zydlewski (2015) Estuaries and Coasts 38: 241?252 (linked below). Code, software tools, and a baseline automated detection approach developed for this effort are available in the PNNL-TUNAMELT GitHub repository, which also provides guidance for getting started with this dataset. For further information, please refer to the GitHub repository, the associated publication, or contact the authors.
Citation Formats
TY - DATA
AB - This labeled dataset contains 107,451 acoustic camera video frames capturing marine life interactions around an underwater tidal turbine. Each frame is annotated with bounding boxes identifying marine life objects as labeled by a fish biologist. Created to support research into automated target detection around underwater turbines, this dataset aims to advance capabilities that enable the safe deployment and operation of marine energy devices. No collisions were observed with the turbine while labeling and analyzing this dataset, and a publication detailing this work will be added once available.
The video data was originally collected in 2010 around Ocean Renewable Power Company's (ORPC) tidal turbine deployment in Cobscook Bay, Maine, USA, with results published in Viehman and Zydlewski (2015) Estuaries and Coasts 38: 241?252 (linked below). Code, software tools, and a baseline automated detection approach developed for this effort are available in the PNNL-TUNAMELT GitHub repository, which also provides guidance for getting started with this dataset. For further information, please refer to the GitHub repository, the associated publication, or contact the authors.
AU - Nowak, Theodore
A2 - Staines, Garrett
A3 - Abdullai, Blerim
DB - Marine and Hydrokinetic Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - MHK
KW - Computer Vision
KW - Dataset
KW - Acoustic Camera
KW - Imaging Sonar
KW - Environmental Monitoring
KW - MHK Monitoring
KW - Collision Risk
KW - PNNL-TUNAMELT
KW - automated detection
KW - marine energy devices
KW - acoustic camera video frames
KW - tidal turbine
KW - code
KW - GitHub
KW - software tools
LA - English
DA - 2025/06/01
PY - 2025
PB - Pacific Northwest National Laboratory
T1 - The PNNL-TUNAMELT Dataset for Automated Detection Around Marine Energy Devices
UR - https://mhkdr.openei.org/submissions/633
ER -
Nowak, Theodore, et al. The PNNL-TUNAMELT Dataset for Automated Detection Around Marine Energy Devices. Pacific Northwest National Laboratory, 1 June, 2025, Marine and Hydrokinetic Data Repository. https://mhkdr.openei.org/submissions/633.
Nowak, T., Staines, G., & Abdullai, B. (2025). The PNNL-TUNAMELT Dataset for Automated Detection Around Marine Energy Devices. [Data set]. Marine and Hydrokinetic Data Repository. Pacific Northwest National Laboratory. https://mhkdr.openei.org/submissions/633
Nowak, Theodore, Garrett Staines, and Blerim Abdullai. The PNNL-TUNAMELT Dataset for Automated Detection Around Marine Energy Devices. Pacific Northwest National Laboratory, June, 1, 2025. Distributed by Marine and Hydrokinetic Data Repository. https://mhkdr.openei.org/submissions/633
@misc{MHKDR_Dataset_633,
title = {The PNNL-TUNAMELT Dataset for Automated Detection Around Marine Energy Devices},
author = {Nowak, Theodore and Staines, Garrett and Abdullai, Blerim},
abstractNote = {This labeled dataset contains 107,451 acoustic camera video frames capturing marine life interactions around an underwater tidal turbine. Each frame is annotated with bounding boxes identifying marine life objects as labeled by a fish biologist. Created to support research into automated target detection around underwater turbines, this dataset aims to advance capabilities that enable the safe deployment and operation of marine energy devices. No collisions were observed with the turbine while labeling and analyzing this dataset, and a publication detailing this work will be added once available.
The video data was originally collected in 2010 around Ocean Renewable Power Company's (ORPC) tidal turbine deployment in Cobscook Bay, Maine, USA, with results published in Viehman and Zydlewski (2015) Estuaries and Coasts 38: 241?252 (linked below). Code, software tools, and a baseline automated detection approach developed for this effort are available in the PNNL-TUNAMELT GitHub repository, which also provides guidance for getting started with this dataset. For further information, please refer to the GitHub repository, the associated publication, or contact the authors.},
url = {https://mhkdr.openei.org/submissions/633},
year = {2025},
howpublished = {Marine and Hydrokinetic Data Repository, Pacific Northwest National Laboratory, https://mhkdr.openei.org/submissions/633},
note = {Accessed: 2025-07-25}
}
Details
Data from Jun 1, 2025
Last updated Jul 18, 2025
Submitted Jun 19, 2025
Organization
Pacific Northwest National Laboratory
Contact
Theodore Nowak
509.375.2531
Authors
Keywords
MHK, Computer Vision, Dataset, Acoustic Camera, Imaging Sonar, Environmental Monitoring, MHK Monitoring, Collision Risk, PNNL-TUNAMELT, automated detection, marine energy devices, acoustic camera video frames, tidal turbine, code, GitHub, software toolsDOE Project Details
Project Name Triton Initiative
Project Lead Samantha Eaves
Project Number FY25 AOP 232611
