ArticlePolymers2025
An Advanced CNN-Based Framework for the Automated Detection of Uncovered PET in Recycling Streams.
Article in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Lightweight YOLO object detectors for PET and HDPE classification in recycling facilities.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Contamination in recycling streams represents one of the most pervasive challenges confronting material recovery facilities (MRFs) globally. Among the various contamination sources in recycling streams, liquid contamination from PET bottles presents particularly severe challenges due to its capacity to spread throughout commingled materials. Object detection using neural networks enables detection at the collection stage of single or mixed recycling streams, allowing for targeted application in the early stage of the recycling cycle. YOLO (you only look once) models and other object detection models are beneficial due to their speed and accuracy in detecting multiple objects at once. This study aimed to design a model to detect contaminated PET bottles in real time. Several YOLO variations and model sizes were trained on a custom dataset with 7130 images. YOLOv8l achieved the highest performance, with mAP@0.5:0.95, mAP@0.5, precision, recall, and F1 score values of 89.7%, 93%, 89%, 88%, and 88%, respectively.
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Registered trials
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 Socratic graph.