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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2022: Machine Learning and Principles and Practice of Knowledge Discovery in Databases pp 137–149Cite as

Combining Image Enhancement Techniques and Deep Learning for Shallow Water Benthic Marine Litter Detection

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1752)

Abstract

The scarcity of information about benthic marine litter especially in developing countries hampers the implementation of targeted actions to minimize the extent of its impacts. This study developed a system using image processing and deep learning methods for detecting/tracking marine macro litter that can efficiently identify and quantify its amount in benthic environments in shallow coastal areas. Shallow underwater litter detection poses several challenges. First is the low quality of images. Second is the difficulty in recognizing litter brought by their varying visual characteristics. Third is the lack of available data for training. Underwater images of litter were collected from marine litter hotspots in coastal areas in southern Philippines. This study experimented with various object detection algorithms. The best object detection model is then paired with various image enhancement techniques to determine the optimal combination. Among the combinations that were tested, YOLOv5n combined with CLAHE gave the best performance for simple binary task (litter or not litter) with a mAP@0.5 of 0.704. Furthermore, the results showed that applying underwater image enhancement techniques provides noticeable improvement for object detection models on detecting marine litter.

Keywords

  • Yolov5
  • Image enhancement
  • Marine litter
  • Object detection

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Authors and Affiliations

  1. AIR-ES Lab, University of the Philippines Mindanao, Davao City, Philippines

    Gil Emmanuel Bancud, Alex John Labanon, Neil Angelo Abreo & Vladimer Kobayashi

Authors
  1. Gil Emmanuel Bancud
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  2. Alex John Labanon
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  3. Neil Angelo Abreo
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  4. Vladimer Kobayashi
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Corresponding author

Correspondence to Gil Emmanuel Bancud .

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Editors and Affiliations

  1. University of Sydney, Sydney, Australia

    Irena Koprinska

  2. University of Bari Aldo Moro, Bari, Italy

    Paolo Mignone

  3. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  4. Warsaw University of Technology, Warsaw, Poland

    Szymon Jaroszewicz

  5. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  6. UniCredit, Rome, Italy

    Francesco Gullo

  7. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  8. Roche, Basel, Switzerland

    Damian Roqueiro

  9. Barcelona Supercomputing Center, Barcelona, Spain

    Gaia Ceddia

  10. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  11. University of Porto, Porto, Portugal

    João Gama

  12. University of Porto, Porto, Portugal

    Rita Ribeiro

  13. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  14. University of Naples Federico II, Naples, Italy

    Elio Masciari

  15. University of North Carolina, Charlotte, USA

    Zbigniew Ras

  16. ICAR-CNR, Rende, Italy

    Ettore Ritacco

  17. University of Pisa, Pisa, Italy

    Francesca Naretto

  18. Aalen University of Applied Sciences, Aalen, Germany

    Andreas Theissler

  19. Warsaw University of Technology, Warszaw, Poland

    Przemyslaw Biecek

  20. KU Leuven, Leuven, Belgium

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  21. University of Duisburg-Essen, Essen, Germany

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  22. Graz University of Technology, Graz, Austria

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  23. AMD, Dublin, Ireland

    Michaela Blott

  24. UniCredit, Rome, Italy

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  25. UniCredit, Milan, Italy

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  26. National Agency for New Technologies, Rome, Italy

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  27. Unicredit, Rome, Italy

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  28. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  29. University of Bari Aldo Moro, Bari, Italy

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  30. University of Lisbon, Lisbon, Portugal

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  31. University of Lisbon, Lisbon, Portugal

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  33. Roche, Basel, Switzerland

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Bancud, G.E., Labanon, A.J., Abreo, N.A., Kobayashi, V. (2023). Combining Image Enhancement Techniques and Deep Learning for Shallow Water Benthic Marine Litter Detection. In: , et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-23618-1_9

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