Environmental Science and Pollution Research

, Volume 26, Issue 17, pp 17091–17099 | Cite as

Identifying floating plastic marine debris using a deep learning approach

  • Kyriaki Kylili
  • Ioannis Kyriakides
  • Alessandro Artusi
  • Constantinos HadjistassouEmail author
Research Article


Estimating the volume of macro-plastics which dot the world’s oceans is one of the most pressing environmental concerns of our time. Prevailing methods for determining the amount of floating plastic debris, usually conducted manually, are time demanding and rather limited in coverage. With the aid of deep learning, herein, we propose a fast, scalable, and potentially cost-effective method for automatically identifying floating marine plastics. When trained on three categories of plastic marine litter, that is, bottles, buckets, and straws, the classifier was able to successfully recognize the preceding floating objects at a success rate of ≈ 86%. Apparently, the high level of accuracy and efficiency of the developed machine learning tool constitutes a leap towards unraveling the true scale of floating plastics.


Image classification Convolutional Neural Networks Data processing Deep learning Marine debris Plastics Monitoring 


Funding information

K. K. acknowledges financial support from the Universitas Foundation, the A. G. Leventis Foundation, and the University of Nicosia. A. A.’s work is financially supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No 739551 (KIOS CoE) and from the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.


  1. Barnes DKA, Galgani F, Thompson RC, Barlaz M (2009) Accumulation and fragmentation of plastic debris in global environments. Philos Trans R Soc Lond B Biol Sci 364:1985–1998CrossRefGoogle Scholar
  2. Chambault P, Vandeperre F, Machete M, Lagoa JC, Pham CK (2018) Distribution and composition of floating macro litter off the Azores archipelago and Madeira (NE Atlantic) using opportunistic surveys. Mar Environ Res 141:225–232CrossRefGoogle Scholar
  3. Cózar A, Echevarría F, González-Gordillo JI, Irigoien X, Úbeda B, Hernández-León S, Palma ÁT, Navarro S, García-de-Lomas J, Ruiz A, Fernández-de-Puelles ML, Duarte CM (2014) Plastic debris in the open ocean. Proc Natl Acad Sci 111:10239–10244CrossRefGoogle Scholar
  4. Deidun A, Gauci A, Lagorio S, Galgani F (2018) Optimising beached litter monitoring protocols through aerial imagery. Mar Pollut Bull 131:212–217CrossRefGoogle Scholar
  5. Eriksen M, Lebreton LCM, Carson HS, Thiel M, Moore CJ, Borerro JC, Galgani F, Ryan PG, Reisser J (2014) Plastic pollution in the world’s oceans: more than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS ONE 9:1–15Google Scholar
  6. González-Fernández D, Hanke G (2017) Toward a harmonized approach for monitoring of riverine floating macro litter inputs to the marine environment. Front Mar Sci 4Google Scholar
  7. Hastie TJ, Tibshirani RJ, Friedman JH (2013) The elements of statistical learning: data mining, inference, and prediction. Springer, New YorkGoogle Scholar
  8. Hengstmann E, Gräwe D, Tamminga M, Fischer EK (2017) Marine litter abundance and distribution on beaches on the Isle of Rügen considering the influence of exposition, morphology and recreational activities. Mar Pollut Bull 115:297–306CrossRefGoogle Scholar
  9. Kylili K, Artusi A, Kyriakides I, Hadjistassou C (2018) Tracking and identifying floating marine debris, 6th International Marine Debris Conference, San Diego, California, USGoogle Scholar
  10. Lebreton L, Slat B, Ferrari F, Sainte-Rose B, Aitken J, Marthouse R, Hajbane S, Cunsolo S, Schwarz A, Levivier A, Noble K, Debeljak P, Maral H, Schoeneich-Argent R, Brambini R, Reisser J (2018) Evidence that the Great Pacific Garbage Patch is rapidly accumulating plastic. Sci Rep 8:4666CrossRefGoogle Scholar
  11. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., 1989. Handwritten digit recognition with a back-propagation network, Proceedings of the 2nd International Conference on Neural Information Processing Systems. MIT Press, pp. 396–404Google Scholar
  12. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324CrossRefGoogle Scholar
  13. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  14. Moy K, Neilson B, Chung A, Meadows A, Castrence M, Ambagis S, Davidson K (2018) Mapping coastal marine debris using aerial imagery and spatial analysis. Mar Pollut Bull 132:52–59CrossRefGoogle Scholar
  15. MSFD Technical Subgroup on Marine Litter (2013) Guidance on monitoring of marine litter in European seas. JRC83985, Joint Research Centre Scientific and Policy Reports, LuxembourgGoogle Scholar
  16. Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345–1359CrossRefGoogle Scholar
  17. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ICLR 2015Google Scholar
  18. Suaria G, Aliani S (2014) Floating debris in the Mediterranean Sea. Mar Pollut Bull 86:494–504CrossRefGoogle Scholar
  19. Sun, C., Shrivastava, A., Singh, S., Gupta, A., 2017. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era, 2017 IEEE International Conference on Computer Vision (ICCV), pp. 843–852Google Scholar
  20. Wang Y, Wang D, Lu Q, Luo D, Fang W (2015) Aquatic debris detection using embedded camera sensors. Sensors 15:3116–3137CrossRefGoogle Scholar
  21. Woodall LC, Sanchez-Vidal A, Canals M, Paterson GLJ, Coppock R, Sleight V, Calafat A, Rogers AD, Narayanaswamy BE, Thompson RC (2014) The deep sea is a major sink for microplastic debris. R Soc Open Sci 1Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Marine & Carbon Lab, Department of EngineeringUniversity of NicosiaNicosiaCyprus
  2. 2.KIOS Research CenterUniversity of CyprusNicosiaCyprus

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