AI-based plastic waste sorting method utilizing object detection models for enhanced classification

Abstract

The export ban on plastic waste by China has brought domestic plastic recycling to the forefront of environmental concerns, with sorting being a crucial step in the recycling process. This study assessed the performance of advanced AI models, Mask R-CNN, and YOLO v8, in enhancing plastic waste sorting. The models were evaluated in terms of accuracy, mean average precision (mAP), precision, recall, F1 score, and inference time, with hyperparameter tuning performed through grid search. Mask R-CNN, with an accuracy of 0.912 and mAP of 0.911, outperformed YOLO v8 in tasks requiring detailed segmentation, despite a longer inference time of 200–350 ms. Conversely, YOLO v8, with an accuracy of 0.867 and mAP of 0.922, excelled in real-time applications owing to its shorter inference time of 80–160 ms. This study underscores the importance of selecting the appropriate model based on specific application requirements.