Sustainability-aware Computer Vision for Scrap Material Recognition in Automated Sorting
Bibliographic record
Abstract
Implementing end-of-life (EoL) management strategies and moving towards sustainable development contribute to mitigating resource depletion and energy consumption. Within this context, scrap material sorting is a crucial step that organizes waste materials for further processing, such as recycling. This research proposes a sustainability-aware computer vision approach for scrap material recognition in automated sorting. First, this study presents a new dataset that includes images of various materials (e.g., aluminum, copper, etc.). To enhance the dataset, multiple data augmentation and preprocessing techniques, such as noise addition and rotation, are applied. Next, it fine-tunes the pre-trained YOLOv5, YOLOv8, YOLOv11, YOLOv12, and RetinaNet models using transfer learning. To promote green machine learning (ML) and align with sustainability, this study validates the models by using metrics related to sustainability factors (e.g., energy consumption and carbon emission) in addition to typical technical metrics, such as mAP@50, Precision, Recall, and F1 Score. Finally, a Pareto analysis is conducted to identify optimal models that balance technical performance and environmental impacts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".