TIPS: Thermal Image based Plastics Sorting

Plastics recycling is a critical ecological and economic solution to manage plastic waste, yet a staggering proportion of plastics from daily use is landfilled or incinerated. A critical step to recycling plastics is our ability to sort plastics by type (HDPE, LDPE, PET, PP, PS, and PVC) at a mixed recycling facility. However, challenges such as sensor system cost, difficulty in data collection, and the dense sampling required for model fine-tuning continue to hinder reliable large-scale deployment and limit progress toward a sustainable circular plastic economy. In this work, we propose a novel physics-informed plastics classification system based on active thermal imaging. Additionally, we present a two-stage training strategy that uses a large quantity of easily generated PDE-based simulated samples for pretraining and fine-tunes the model to real-world data distributions using only a sparse set of samples. We validate the efficacy of the proposed approach on real-world plastic samples. Thus, we introduce Thermal Imaging based Plastic Sorting (TIPS), a system that achieves up to 100% and 94.7% accuracy in plastic type classification for black and white plastics, respectively.