Plastic water bottle detection model using computer vision in aquatic environments.

Journal: Scientific reports
Published Date: Jul 10, 2025

Abstract

Watershed macrotrash contamination is difficult to measure and requires tedious and labor-intensive processes. This work proposes an automated approach to waste counting, focusing on using computer vision, deep learning, and object tracking algorithms to acquire accurate counts of plastic bottles as they advect down rivers and streams. By using a combination of several publicly available labeled trash and plastic bottle image datasets, the model was trained to achieve high performance with the YOLOv8 object detection model. This was paired with the Norfair object tracking library and a novel post-processing algorithm to filter out false positives. The model performed extremely accurately over the test scenarios with just one false positive and recalls in excess of 0.947.

Authors

  • Andrew Heller
    Catholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
  • Matthew Jacobs
    Catholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
  • Gilberto Acosta-González
    SECIHTI - Unidad de Ciencias del Agua, Centro de Investigación Científica de Yucatán A.C. (CICY), Cancún, 77500, Mexico.
  • Anna Basola
    Catholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States.
  • Jessica Beck
    Catholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States.
  • Wesley Garnes
    Catholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
  • Jarelys A Hernández Molina
    University of Puerto Rico Mayagüez Campus, Department of Geology, Mayagüez, 00681, United States.
  • Alanso Johnson
    University of Maryland, Department of Aerospace Engineering, College Park, 20740, United States.
  • Rebecca Kiriazes
    Catholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States.
  • Melissa Lenczewski
    Northern Illinois University, Institute for the Study of the Environment, Sustainability and Energy, DeKalb, 60115, United States.
  • Ellen O'Brien
    Catholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
  • Grace Pooley Deans
    Catholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States.
  • Rhea Roxy
    Catholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
  • Blaise Trapani
    Catholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
  • Jason H Davison
    Catholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States. davisonj@cua.edu.

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