Robust Identification of Polyethylene Terephthalate (PET) Plastics through Bayesian Decision

Abstract

Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.

Computer Vision based Polyethylene Terephthalate (PET) Sorting for Waste Recycling

“…In the literature, several PET sorting approaches have been reported. Among the reported works, some works designed a handcrafted feature suitable for PET categorization [28] [29] [30]. Other works used available generic handcrafted features [31] [32].…”

Mechanical Properties of Composite Materials from Waste Poly(ethylene terephthalate) Reinforced with Glass Fibers and Waste Window Glass

“…Among municipal wastes, plastics are the most common material that can be easily recycled, and poly(ethylene terephthalate) (PET) is one of its major types. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high-grade recyclable material [6].…”

Spectroscopic Identification of Environmental Microplastics

“…All these factors further hinder the automatic identification and increase manual labor during the spectral analysis. For the distortions of spectra, due to the diversity of environmental plastics samples, it is not easy to build an effective model to analyze the sample's compositions [18][19]. Several reports have proposed the recognition algorithms for automatic identification of MPs category, such as the Principal Component Analysis (PCA) [20] or Random Decision Forest (RDF) [21].…”