Lightweight YOLO object detectors for PET and HDPE classification in recycling facilities.

Growing volumes of plastic waste highlight the need for reliable sorting technologies. This study examines whether lightweight YOLO object detectors can distinguish polyethylene terephthalate (PET) bottles from high-density polyethylene (HDPE) bottles, two common packaging materials that must be separated for recycling. Two public PET and HDPE datasets were augmented offline to simulate the visual variation typical of industrial sorting lines, and eleven compact YOLO models-nano, tiny and small versions across versions 7-12-were fine-tuned on these data. Their accuracy was quantified using mean average precision (mAP) at multiple intersection-over-union thresholds and precision, recall and F1 scores; their efficiency was evaluated by inference time, size and floating-point operations. All models achieved mAP@0.5 above 99.2% and F1 scores exceeding 98% on test data. Among them, YOLOv11n delivered the best trade-off between accuracy and speed, it achieved mAP@0.5:0.95 of 93.7%, processed 640 × 640 pixel images in around 38 ms on a CPU, and required just 5.5 MB of memory. These findings suggest that accurate polymer discrimination is possible on modest hardware, enabling resource-constrained recycling facilities to reduce mis-sorting, trim costs and support a circular economy.