Smart Environmental Monitoring: Real-Time Plastic Waste Detection Using YOLOv8
Bibliographic record
Abstract
Plastic pollution is one of the most pressing environmental challenges of the 21st century, with over 380 million tons produced annually and nearly 8 million tons entering oceans each year. Conventional recycling methods, reliant on manual sorting, are inefficient, error-prone, and unsustainable. This research presents a real-time plastic detection and classification system leveraging Artificial Intelligence (AI) and computer vision. Using YOLOv8 for object detection, OpenCV for preprocessing, and Flask for deployment, the system was trained on a custom dataset of seven plastic categories: PET, HDPE, PVC, LDPE, PP, PS, and Others. Data preprocessing included normalization, resizing, Gaussian blur, and augmentation to enhance robustness. Transfer learning with pre-trained weights yielded strong results, with the model achieving 92% accuracy, over 40 FPS processing speed, and consistent performance under varying lighting conditions. PET and HDPE achieved the highest classification accuracy, while some overlap occurred between PVC and PS. The integration of YOLOv8 with a user-friendly web application demonstrates how AI can reduce human effort, improve sorting accuracy, and enhance recycling efficiency. By aligning with global sustainability goals, this study underscores the transformative potential of AI-powered plastic classification systems in reducing pollution, conserving resources, and supporting sustainable urbandevelopment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".