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Detecting underwater sea litter using deep neural networks: An initial study
Music, J.; Kruzic, S.; Stancic, I.; Alexandrou, F. (2020). Detecting underwater sea litter using deep neural networks: An initial study, in: 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), 23-26 Sept. 2020, Split, Croatia. pp. 1-6. https://dx.doi.org/10.23919/splitech49282.2020.9243709
In: (2020). 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), 23-26 Sept. 2020, Split, Croatia. IEEE: [s.l.]. ISBN 9781728173634.
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| Beschikbaar in | Auteurs |
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Documenttype: Congresbijdrage
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| Trefwoorden |
Classification
Detection
Neural networks
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| Author keywords |
Marine litter, underwater images |
| Auteurs | | Top |
- Music, J.
- Kruzic, S.
- Stancic, I.
- Alexandrou, F.
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| Abstract |
The world's seas and the oceans are under constant negative pressure caused by human activity. It is estimated that more than 150 million tonnes of litter will be accumulated in the world's oceans until 2025, while up to 12.7 million tonnes of litter will be added to the sea every year. Besides ecology-related issues, marine litter can also hurt the economy of the affected areas. Detection and classification of sea litter thus becomes a first step in tracking the litter and consequently a basis for the development of any automatic or human-based marine litter retrieval system.Modern convolutional neural networks are a logical choice for detection and classification algorithms since they have proven themselves time after time in image-based machine learning tasks. Nevertheless, according to the available literature, the application of such neural networks in underwater images for marine litter detection (and classification) has started just recently. Thus, the paper carries out an initial study on the performance of such detection and classification system constructed in several ways and with several architectures, as well as using several sources of training data. It is shown that obtained validation accuracy is around 88% and test accuracy around 85%, depending on the used architecture, and that inclusion of synthetically generated images reduces the network performance on real-world image dataset. |
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