Summary of Benthicnet: a Global Compilation Of Seafloor Images For Deep Learning Applications, by Scott C. Lowe et al.
BenthicNet: A global compilation of seafloor images for deep learning applications
by Scott C. Lowe, Benjamin Misiuk, Isaac Xu, Shakhboz Abdulazizov, Amit R. Baroi, Alex C. Bastos, Merlin Best, Vicki Ferrini, Ariell Friedman, Deborah Hart, Ove Hoegh-Guldberg, Daniel Ierodiaconou, Julia Mackin-McLaughlin, Kathryn Markey, Pedro S. Menandro, Jacquomo Monk, Shreya Nemani, John O’Brien, Elizabeth Oh, Luba Y. Reshitnyk, Katleen Robert, Chris M. Roelfsema, Jessica A. Sameoto, Alexandre C. G. Schimel, Jordan A. Thomson, Brittany R. Wilson, Melisa C. Wong, Craig J. Brown, Thomas Trappenberg
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Advances in underwater imaging have led to the collection of extensive seafloor image datasets, crucial for monitoring benthic ecosystems. However, the analysis capacity has not kept pace with data collection, hindering the mobilization of this information. Machine learning approaches can increase efficiency, but large and consistent datasets are scarce. This paper presents BenthicNet: a global compilation of seafloor imagery designed to support image recognition models. The initial dataset consists of over 11.4 million images, representing diverse seafloor environments. These images are accompanied by 3.1 million annotations translated to the CATAMI scheme, spanning 190,000 images. A large deep learning model was trained on this compilation and preliminary results suggest utility for automating image analysis tasks. The compilation and model are openly available for reuse. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a huge library of underwater photos that can help us understand the health of our oceans. Unfortunately, it takes too long to look through all these photos, which is important because it helps us monitor important ocean ecosystems. Scientists have developed new ways to analyze these photos using machine learning, but they don’t have enough data to make this process work well. This paper presents a huge collection of underwater photos and the tools needed to analyze them. The goal is to help scientists quickly and easily identify what’s happening in these photos, which will help us better understand our oceans. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
