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Construction of a Real-Time Detection for Floating Plastics in a Stream Using Video Cameras and Deep Learning.

Hankyu Lee1, Seohyun Byeon2, Jin Hwi Kim3

  • 1Department of Civil and Environmental Engineering, Konkuk University-Seoul, 120, Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary

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This study developed a deep learning model for real-time detection of floating plastic debris in rivers. The model accurately identifies common plastics, bottles, film, and fragments, aiding pollution monitoring.

Area of Science:

  • Environmental Science
  • Computer Science
  • Machine Learning

Background:

  • Rivers are key pathways for plastic debris transport to marine ecosystems.
  • Accurate quantification of surface water plastic is crucial for environmental impact assessments.
  • Real-time monitoring of plastic pollution in natural environments, especially post-rainfall, is limited.

Purpose of the Study:

  • To develop a real-time visual recognition model for detecting floating plastic debris using deep learning.
  • To implement a multi-class classification system for various plastic types.
  • To evaluate the practical applicability and portability of the model for freshwater pollution monitoring.

Main Methods:

  • Utilized the YOLOv8 algorithm, specifically YOLOv8-nano, for object detection.
Keywords:
YOLOdeep learningobject detectionplastic debris monitoringwater management

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  • Trained the model using field video data of floating plastic debris.
  • Classified four types of plastic debris: common plastics, plastic bottles, plastic film and vinyl, and fragmented plastics.

    • Main Results:

    • The YOLOv8 model achieved high performance with an F1-score of 0.982 (validation) and 0.980 (testing).
    • Detection performance showed excellent mAP scores: 0.992 (IoU = 0.5) and 0.714 (IoU = 0.5:0.05:0.95).
    • The model demonstrated robust classification and detection capabilities for floating plastic debris.

    Conclusions:

    • The developed deep learning model shows significant potential for real-time assessment of plastic debris discharge in rivers.
    • Findings support the model's utility in informing effective plastic pollution management strategies.
    • Further improvements in tracking labels and data collection are recommended to enhance precision in freshwater monitoring applications.