Development of an EfficientNetB0-Based Artificial Intelligence Model for Detection, Classification, and Separation of Polyethylene Terephthalate from Mixed Plastic Waste Streams

1. The paper proposes an EfficientNetB0-based six-polymer classification system for HDPE, LDPE, PET, PP, PS, and PVC, followed by practical PET/non-PET separation for recycling applications.
2. The paper improves dataset reliability through exact-duplicate removal, controlled oversampling, class weighting, augmentation, label smoothing, regularization, and multi-stage fine-tuning, reducing data leakage and class-bias problems.
3. The paper demonstrates strong PET-specific performance, achieving 96.84% accuracy, 94.85% precision, 92.47% recall, 93.65% F1-score, and 99.35% ROC-AUC on 1,109 independent test images.
4. The paper provides a practical pathway toward edge-based and industrial PET sorting, with future integration into cameras, conveyors, IoT monitoring, and robotic or pneumatic separation systems.