Evidence map›Paper›PMID 40640371›Full record

ArticleScientific reports2025

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

Andrew Heller, Matthew Jacobs, Gilberto Acosta-González, Anna Basola, Jessica Beck, Wesley Garnes, Jarelys A Hernández Molina, Alanso Johnson, Rebecca Kiriazes, Melissa Lenczewski and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Andrew HellerCatholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
Matthew JacobsCatholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
Gilberto Acosta-GonzálezSECIHTI - Unidad de Ciencias del Agua, Centro de Investigación Científica de Yucatán A.C. (CICY), Cancún, 77500, Mexico.
Anna BasolaCatholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States.
Jessica BeckCatholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States.
Wesley GarnesCatholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
Jarelys A Hernández MolinaUniversity of Puerto Rico Mayagüez Campus, Department of Geology, Mayagüez, 00681, United States.
Alanso JohnsonUniversity of Maryland, Department of Aerospace Engineering, College Park, 20740, United States.
Rebecca KiriazesCatholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States.
Melissa LenczewskiNorthern Illinois University, Institute for the Study of the Environment, Sustainability and Energy, DeKalb, 60115, United States.
Ellen O'BrienCatholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
Grace Pooley DeansCatholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States.
Rhea RoxyCatholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
Blaise TrapaniCatholic University of America, Department of Electrical Engineering and Computer Science, Washington D.C., 20064, United States.
Jason H DavisonCatholic University of America, Department of Civil and Environmental Engineering, Washington D.C., 20064, United States. davisonj@cua.edu.

Funding

Office of Naval Research N00014-22-1-2410
6 · The paper itself

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.

Indexed as

Computer visionMacroplasticsRiverWater bottle

Identifiers

PMID40640371
PMCPMC12246223

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.