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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2405.05241 |
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| _version_ | 1866910833459593216 |
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| author | Lowe, Scott C. Misiuk, Benjamin Xu, Isaac Abdulazizov, Shakhboz Baroi, Amit R. Bastos, Alex C. Best, Merlin Ferrini, Vicki Friedman, Ariell Hart, Deborah Hoegh-Guldberg, Ove Ierodiaconou, Daniel Mackin-McLaughlin, Julia Markey, Kathryn Menandro, Pedro S. Monk, Jacquomo Nemani, Shreya O'Brien, John Oh, Elizabeth Reshitnyk, Luba Y. Robert, Katleen Roelfsema, Chris M. Sameoto, Jessica A. Schimel, Alexandre C. G. Thomson, Jordan A. Wilson, Brittany R. Wong, Melisa C. Brown, Craig J. Trappenberg, Thomas |
| author_facet | Lowe, Scott C. Misiuk, Benjamin Xu, Isaac Abdulazizov, Shakhboz Baroi, Amit R. Bastos, Alex C. Best, Merlin Ferrini, Vicki Friedman, Ariell Hart, Deborah Hoegh-Guldberg, Ove Ierodiaconou, Daniel Mackin-McLaughlin, Julia Markey, Kathryn Menandro, Pedro S. Monk, Jacquomo Nemani, Shreya O'Brien, John Oh, Elizabeth Reshitnyk, Luba Y. Robert, Katleen Roelfsema, Chris M. Sameoto, Jessica A. Schimel, Alexandre C. G. Thomson, Jordan A. Wilson, Brittany R. Wong, Melisa C. Brown, Craig J. Trappenberg, Thomas |
| contents | Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse at https://doi.org/10.20383/103.0614. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05241 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BenthicNet: A global compilation of seafloor images for deep learning applications Lowe, Scott C. Misiuk, Benjamin Xu, Isaac Abdulazizov, Shakhboz Baroi, Amit R. Bastos, Alex C. Best, Merlin Ferrini, Vicki Friedman, Ariell Hart, Deborah Hoegh-Guldberg, Ove Ierodiaconou, Daniel Mackin-McLaughlin, Julia Markey, Kathryn Menandro, Pedro S. Monk, Jacquomo Nemani, Shreya O'Brien, John Oh, Elizabeth Reshitnyk, Luba Y. Robert, Katleen Roelfsema, Chris M. Sameoto, Jessica A. Schimel, Alexandre C. G. Thomson, Jordan A. Wilson, Brittany R. Wong, Melisa C. Brown, Craig J. Trappenberg, Thomas Computer Vision and Pattern Recognition Machine Learning Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse at https://doi.org/10.20383/103.0614. |
| title | BenthicNet: A global compilation of seafloor images for deep learning applications |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.05241 |