###### Abstract  
Plastics, with their versatility and cost-effectiveness, have become indispensable materials across various industries. However, the improper disposal and mismanagement of plastic waste have led to significant environmental issues, including pollution, habitat destruction, and threats to wildlife. To address these challenges, numerous methods for plastic waste sorting and recycling have been developed. While conventional techniques like near-infrared spectroscopy (NIRS) have been effective to some extent, they face difficulties in accurately classifying chemically similar samples, such as polyethylene terephthalate (PET) and PET-glycol (PET-G), which have similar chemical compositions but distinct physical characteristics. This paper introduces an approach that adapts image sensors and deep learning object detection algorithms; specifically, the You Only Look Once (YOLO) model, to enhance plastic waste classification based on the shape of the waste. Unlike conventional methods that rely solely on spectral analysis, our methodology aims to significantly improve the accuracy and efficiency of classifying plastics, especially when dealing with materials having similar chemical compositions but differing physical attributes. The system developed using image sensors and the YOLO model proves to be not only effective but also scalable and adaptable for various industrial and environmental applications. In our experiments, the results are strikingly effective. We achieved a classification accuracy rate exceeding 91.7% mean Average Precision (mAP) in distinguishing between PET and PET-G, surpassing conventional techniques by a considerable margin. The implications of this research extend far and wide. By enhancing the accuracy of plastic waste sorting and reducing misclassification rates, we can significantly boost recycling efficiency. The proposed approach contributes to a more sustainable and efficient plastic waste management system, alleviating the strain on landfills and mitigating the environmental impact of plastic waste, contributing to a cleaner and more sustainable environment. plastic waste classification; recycling efficiency; sustainable waste management; computer vision +
Footnote †: journal: Medical  
variations in the absorption efficiency due to differences in the chemical composition of target samples [10,11], presenting challenges when applied to chemically similar samples. Therefore, there is a critical need for innovative and accurate plastic waste classification methods that can overcome the limitations of techniques and enable efficient recycling. For example, polyethylene terephthalate (PET) and polyethylene terephthalate glycol (PET-G) are both polyester-based plastics used in various applications, but they exhibit differences in their physical properties [12]. Particularly, PET has a higher melting point of approximately 245-255 \({}^{\circ}\)C, while PET-G has a lower melting point of around 70-80 \({}^{\circ}\)C. Consequently, when PET and PET-G are mixed for recycling, it leads to a significant reduction in recycling purity. Recognizing challenges, the California Legislature has passed a bill to revise the definition of PET (polyethylene terephthalate) by excluding PET-G, a glycol-modified version, to reduce contamination in recycling [13]. Moreover, although accurate information on PET-G in waste plastics is currently unavailable, it is anticipated that the global market for PET-G will experience steady growth, increasing from USD 2748 million in 2023 to USD 3819 million in 2033, with a compound annual growth rate (CAGR) of 3.3% [14]. Considering their distinct physical properties, PET and PET-G are commonly used in different forms. For instance, PET is widely used in beverage containers, while PET-G is commonly applied in electronic product packaging. Consequently, recycling facilities predominantly receive PET in the form of beverage containers and PET-G in the shape of rectangular packaging materials.