Data

OzFish Dataset - Machine learning dataset for Baited Remote Underwater Video Stations

Australian Institute of Marine Science
Australian Institute of Marine Science (AIMS) ; Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25845/5e28f062c5097&rft.title=OzFish Dataset - Machine learning dataset for Baited Remote Underwater Video Stations&rft.identifier=https://doi.org/10.25845/5e28f062c5097&rft.publisher=Australian Institute of Marine Science (AIMS)&rft.description=This dataset has been developed as part of the Australian Research Data Commons Data Discoveries program (https://ardc.edu.au/project/machine-learning-dataset-creation-for-australian-fish-species-from-baited-remote-underwater-videos-bruv/), with the aim to futher advance research into machine learning for the automated detection of fish from video. The dataset was generated from over 3000 videos which were historically analysed with the Event Measure software package and sourced from the Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University of Technology. The dataset is comprised of the following: - ~80k labelled crops of fish extracted from the videos, from over 500 species, 200 genera and 70 families - ~45k bounding box annotations (suitable for YOLO,RetinaNet) of fish/no fish across 1800 framesMaintenance and Update Frequency: asNeededStatement: Videos were sourced from partner organisation's and had been historically analysed with the Event Measure (https://www.seagis.com.au/event.html) software package. Crops of fish were identified using the length measurement information from the Event Measure output files, then cropped and associated with the labels given at time of analysis. Bounding boxes of fish were generated by random sampling frames from the historical video archive that had associated measurements, and having them analysed on the Amazon Sagemaker Ground Truth platform (https://aws.amazon.com/sagemaker/groundtruth/). The bounding boxes are combined results from multiple observers.&rft.creator=Australian Institute of Marine Science (AIMS) &rft.creator=Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University &rft.date=2024&rft.coverage=westlimit=112.93945312500001; southlimit=-24.126701958681682; eastlimit=137.54882812500003; northlimit=-10.228437266155943&rft.coverage=westlimit=112.93945312500001; southlimit=-24.126701958681682; eastlimit=137.54882812500003; northlimit=-10.228437266155943&rft_rights=Creative Commons Attribution 3.0 Australia License http://creativecommons.org/licenses/by/3.0/au/&rft_rights=Use Limitation: All AIMS data, products and services are provided as is and AIMS does not warrant their fitness for a particular purpose or non-infringement. While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.&rft_rights=Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University. (2019). OzFish Dataset - Machine learning dataset for Baited Remote Underwater Video Stations . https://doi.org/10.25845/5e28f062c5097, accessed[date-of-access].&rft_subject=oceans&rft.type=dataset&rft.language=English Access the data
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Open Licence view details
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Creative Commons Attribution 3.0 Australia License
http://creativecommons.org/licenses/by/3.0/au/

Use Limitation: All AIMS data, products and services are provided "as is" and AIMS does not warrant their fitness for a particular purpose or non-infringement. While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.

Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: "Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University. (2019). OzFish Dataset - Machine learning dataset for Baited Remote Underwater Video Stations . https://doi.org/10.25845/5e28f062c5097, accessed[date-of-access]".

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Brief description

This dataset has been developed as part of the Australian Research Data Commons Data Discoveries program (https://ardc.edu.au/project/machine-learning-dataset-creation-for-australian-fish-species-from-baited-remote-underwater-videos-bruv/), with the aim to futher advance research into machine learning for the automated detection of fish from video. The dataset was generated from over 3000 videos which were historically analysed with the Event Measure software package and sourced from the Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University of Technology. The dataset is comprised of the following: - ~80k labelled crops of fish extracted from the videos, from over 500 species, 200 genera and 70 families - ~45k bounding box annotations (suitable for YOLO,RetinaNet) of fish/no fish across 1800 frames

Lineage

Maintenance and Update Frequency: asNeeded
Statement: Videos were sourced from partner organisation's and had been historically analysed with the Event Measure (https://www.seagis.com.au/event.html) software package. Crops of fish were identified using the length measurement information from the Event Measure output files, then cropped and associated with the labels given at time of analysis. Bounding boxes of fish were generated by random sampling frames from the historical video archive that had associated measurements, and having them analysed on the Amazon Sagemaker Ground Truth platform (https://aws.amazon.com/sagemaker/groundtruth/). The bounding boxes are combined results from multiple observers.

Notes

Credit
Australian Research Data Commons, Data Discoveries program, Machine learning dataset creation for Australian fish species from Baited Remote Underwater Videos (BRUV), Australia.
Credit
Bond, T - University of Western Australia (UWA)
Credit
Langlios, T - University of Western Australia (UWA)
Credit
Harvey, E - Curtin University of Technology

Modified: 17 10 2024

This dataset is part of a larger collection

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137.54883,-10.22844 137.54883,-24.1267 112.93945,-24.1267 112.93945,-10.22844 137.54883,-10.22844

125.244140625,-17.177569612419

text: westlimit=112.93945312500001; southlimit=-24.126701958681682; eastlimit=137.54882812500003; northlimit=-10.228437266155943

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Other Information
Marrable, D., Tippaya, S., Barker, K., Harvey, E., Beirwagen, S., Wyatt, M., Bainbridge, S., Stowar, M. (2023) Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS. Frontiers in Marine Science. 10. https://doi.org/10.3389/fmars.2023.1171625

doi : https://doi.org/10.3389/fmars.2023.1171625

Marrable, D., Barker, K., Tippaya, S., Wyatt, M., Bainbridge, S., Stowar, M., & Larke, J. (2022). Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning. Frontiers in Marine Science, 9. https://www.frontiersin.org/articles/10.3389/fmars.2022.944582

uri : https://www.frontiersin.org/articles/10.3389/fmars.2022.944582

Codebase used to generate fish crops from event measure files

uri : https://github.com/marrabld/open_fish_classifier

GitHub Repository - OzFish - Public dataset of Australian fish species for advancing machine learning research

uri : https://github.com/open-AIMS/ozfish

Identifiers
  • global : 38c829d4-6b6d-44a1-9476-f9b0955ce0b8
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