Miski Adnan, Bafail Omer
Department of Industrial Engineering, Faculty of Engineering-Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Polymers (Basel). 2025 Jun 22;17(13):1736. doi: 10.3390/polym17131736.
Contamination in recycling streams represents one of the most pervasive challenges confronting material recovery facilities (MRFs) globally. Among the various contamination sources in recycling streams, liquid contamination from PET bottles presents particularly severe challenges due to its capacity to spread throughout commingled materials. Object detection using neural networks enables detection at the collection stage of single or mixed recycling streams, allowing for targeted application in the early stage of the recycling cycle. YOLO (you only look once) models and other object detection models are beneficial due to their speed and accuracy in detecting multiple objects at once. This study aimed to design a model to detect contaminated PET bottles in real time. Several YOLO variations and model sizes were trained on a custom dataset with 7130 images. YOLOv8l achieved the highest performance, with mAP@0.5:0.95, mAP@0.5, precision, recall, and F1 score values of 89.7%, 93%, 89%, 88%, and 88%, respectively.
回收流中的污染物是全球材料回收设施（MRF）面临的最普遍挑战之一。在回收流的各种污染源中，PET瓶的液体污染因其能够在混合材料中扩散而带来特别严峻的挑战。使用神经网络进行目标检测能够在单一或混合回收流的收集阶段进行检测，从而在回收周期的早期阶段实现有针对性的应用。YOLO（你只看一次）模型和其他目标检测模型因其能够同时快速准确地检测多个目标而具有优势。本研究旨在设计一种实时检测受污染PET瓶的模型。在一个包含7130张图像的自定义数据集上对几种YOLO变体和模型大小进行了训练。YOLOv8l表现最佳，其mAP@0.5:0.95、mAP@0.5、精度、召回率和F1分数分别为89.7%、93%、89%、88%和88%。
