Automated Identification of Used Beverage Cans for Deposit Return using Deep Learning Methods
Bibliographic record
Abstract
Accurate sorting of recyclable materials, especially aluminum, is an important process within municipal Material Recovery Facilities (MRFs) as it has a high scrap value and is easily recycled, keeping it out of landfills. Additionally, in jurisdictions that have deposit return programs, MRF operators may return permitted cans and collect the higher deposit value, thus increasing profits. This interest in accurate sorting creates an ideal environment for computer vision and deep learning applications, specifically, the classification and sorting of cans with higher accuracy than human sorters, which are often central to this process. In this work, a can classification dataset was created following deposit return program definitions used in Ontario, Canada. The dataset contains images of returnable and non- returnable cans. Neural networks based on Mask R-CNN are then trained to classify can images as returnable or non-returnable. The neural networks achieve excellent results, with over 99% class accuracy on the testing dataset. Lastly, recommendations for future work and recommendations for system installation and integration are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".