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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".