Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife?
With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Ar...
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| Published in | Marine pollution bulletin Vol. 181; p. 113853 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.08.2022
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| Subjects | |
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| Abstract | With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems.
Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life.
•Deep learning to distinguish between synthetic debris and marine life•Original database of underwater imagery•Convolutional neural network binary classification |
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| AbstractList | With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life. With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life. •Deep learning to distinguish between synthetic debris and marine life•Original database of underwater imagery•Convolutional neural network binary classification With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life.With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life. |
| ArticleNumber | 113853 |
| Author | Moorton, Zoe Woo, Wai Lok Kurt, Zeyneb |
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| Cites_doi | 10.1016/j.neucom.2015.09.116 10.1016/j.marpolbul.2016.06.034 10.3390/rs13173401 10.1038/ncomms15611 10.1016/j.envpol.2021.116490 10.1016/j.marpolbul.2011.09.010 10.1016/j.scitotenv.2020.137768 10.1016/j.marpolbul.2013.01.033 10.1016/j.chemosphere.2020.126569 10.1126/science.abe5041 10.1016/j.jhazmat.2020.124020 10.1111/j.1740-9713.2017.01072.x 10.1007/s11356-019-05148-4 10.1021/es0010498 10.7185/geochemlet.1829 10.1007/s10924-007-0074-3 10.1016/j.scitotenv.2019.07.246 |
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| Keywords | Deep learning Neural network Artificial intelligence Ocean pollution Marine debris |
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| Snippet | With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can... |
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| SubjectTerms | aquatic organisms Artificial intelligence Deep learning industry Marine debris marine pollution Neural network neural networks Ocean pollution synthetic products wildlife |
| Title | Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife? |
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