A Garbage Classification and Environmental Impact Assessment Model Based on Image Recognition and Artificial Intelligence
Bibliographic record
Abstract
With the rapid urbanization process, waste management has become a significant environmental issue globally.Waste sorting, as an effective method of resource recycling and environmental protection, has gradually become a key solution to the waste pollution problem.Traditional waste classification methods rely on manual labor, which is inefficient and prone to errors, making them inadequate for modern urban waste management.In recent years, image recognition and artificial intelligence (AI)-based methods for waste classification have gained widespread attention, with deep learning techniques, particularly Convolutional Neural Networks (CNNs), showing great potential in waste sorting.However, existing research on waste classification models faces challenges such as imperfect network structures, insufficient training data, and poor environmental adaptability, which limit their application in complex environments.This study proposes a waste classification model based on image recognition and AI to enhance classification accuracy and efficiency.First, an improved PCANet and SDenseNet network structure is combined to propose a new feature extraction and representation method, enhancing the model's feature learning ability.Secondly, a layered learning strategy, combined with the traditional backpropagation algorithm, is used to optimize the training process and improve learning efficiency.Finally, experimental results demonstrate that the proposed waste classification model significantly outperforms traditional models in classification accuracy and processing capability in various environments, providing a new solution for the advancement of waste classification technologies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".