TIPS: Thermal Image based Plastics Sorting

Abstract

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 Image based Plastics Sorting (TIPS), a system that achieves up to 100% and 94.7% accuracy in plastic type classification for black and white plastics, respectively.

TIPS Overview

Thermal response of the plastics

During forward phase, TIPS extracts the thermal properties from the plastic thermal response for classification.

During training stage 1, TIPS’s encoder and PINN head learn how to extract thermal properties following heat transfer partial differential equations (PDE).

During training stage 2, using the thermal properties extracted using the frozen encoder, TIPS’s classifier learns how to classify plastics.