Abstract
Irrespective of how plastics litter the coastline or enter the sea, they pose a major threat to birds and marine life alike. In this study, an artificial intelligence tool was used to create an image classifier based on a convolutional neural network architecture that utilises the bottleneck method. The trained bottleneck method classifier was able to categorise plastics encountered either at the shoreline or floating at the sea surface into eight distinct classes, namely, plastic bags, bottles, buckets, food wrappings, straws, derelict nets, fish, and other objects. Discerning objects with a success rate of 90%, the proposed deep learning approach constitutes a leap towards the smart identification of plastics at the coastline and the sea. Training and testing loss and accuracy results for a range of epochs and batch sizes have lent credibility to the proposed method. Results originating from a resolution sensitivity analysis demonstrated that the prediction technique retains its ability to correctly identify plastics even when image resolution was downsized by 75%. Intelligent tools, such as the one suggested here, can replace manual sorting of macroplastics from human operators revealing, for the first time, the true scale of the amount of plastic polluting our beaches and the seas.
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Acknowledgements
The authors would like to thank Algalita non-profit organisation for kindly sharing with us marine debris images and videos acquired during their boat expeditions.
Funding
K. K. acknowledges financial support from the A. G. Leventis Foundation. A. A.’s work is financially supported by the European Union, Horizon 2020 research and innovation programme under grant agreement no. 739578 also complemented by the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.
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Kylili, K., Hadjistassou, C. & Artusi, A. An intelligent way for discerning plastics at the shorelines and the seas. Environ Sci Pollut Res 27, 42631–42643 (2020). https://doi.org/10.1007/s11356-020-10105-7
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DOI: https://doi.org/10.1007/s11356-020-10105-7