Deep learning for detecting macroplastic litter in water bodies: A review

Tianlong Jia*, Zoran Kapelan, Rinze de Vries, Paul Vriend, Eric Copius Peereboom, Imke Okkerman, Riccardo Taormina

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

17 Citations (Scopus)
310 Downloads (Pure)

Abstract

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter.

Original languageEnglish
Article number119632
Number of pages16
JournalWater Research
Volume231
DOIs
Publication statusPublished - 2023

Keywords

  • Artificial intelligence
  • Computer vision
  • Environmental monitoring
  • Macroplastics
  • Neural networks
  • Pollution

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