Automatic Bag-breaking Classification and Collection System for Kitchen Waste Based on OpenCV
Bibliographic record
Abstract
Traditional kitchen waste disposal requires manual separation of plastic bags from food waste, which is cumbersome and unhygienic. However, the capacity, shape, and size of the plastic bags used to hold kitchen waste are different, resulting in an unstable bottom position of the plastic bags when putting them in. Therefore, it is difficult to break plastic bags of any size by using traditional mechanical devices. This paper introduces a computer vision-based automatic bag-breaking classification and collection system for kitchen waste, which aims to solve various pain points in the process of food waste classification and placement. The system involves computer vision, single-chip microcomputer, and internet of things (IoT) technology. When residents dispose of kitchen waste, they only need to hang the bag on the device, the system will visually judge the size and position of the plastic bag, and then control the motor to adjust the plastic bag to the corresponding position to break the bag, and the kitchen waste will fall into the kitchen waste in the garbage bin, it is judged by visual inspection whether the bag breaking is completed, and if it is completed, the plastic bag is thrown into another garbage bin.
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".