## 2 Background  
### Overview of Plastic Waste Classification Techniques  
Plastic waste classification techniques play a vital role in the recycling and management of plastic waste (Figure 1a), facilitating the efficient separation and processing of different types of plastics. Conventional approaches to plastic waste classification have predominantly relied on near-infrared spectroscopy (NIRS). This technique utilizes the unique infrared spectra exhibited by various plastic polymers to distinguish and sort them. NIR-based classification has shown promising results in accurately identifying plastics such as polypropylene (PP), polystyrene (PS), polymethyl methacrylate (PMMA), polyethylene (PET), polyethylene (PE), and acrylonitrile butadiene styrene (ABS). However, the effectiveness of NIRS is limited when it comes to plastics with similar compositions but different physical attributes (Figure 1b), making it challenging to differentiate them solely based on their infrared spectra.  
Conventional Approach Using Near-Infrared Spectroscopy for Plastic Waste Classification and Limitations and Challenges of Near-Infrared Spectroscopy  
One significant limitation of NIRS-based plastic waste classification is its reliance solely on compositional analysis, neglecting the influence of the shape and form of the plastics. Plastics with similar compositions but different shapes, such as PET and PET-G, pose a challenge for NIRS-based classification. Despite their comparable molecular compositions, PET and PET-G can have distinct physical properties due to the addition of glycol in PET-G, resulting in differences in transparency and flexibility. As NIRS primarily focuses on molecular composition, it struggles to accurately differentiate PET and PET-G, leading to misclassifications during the recycling process. Consequently, the recycling efficiency and the quality of recycled plastic products can be compromised. Moreover, NIRS requires  
Figure 1: PET and PET-G classification system: (**a**) overall classification process (**b**) target to classify plastic component.  
specialized equipment and expertise for spectral analysis, making it less accessible and cost-effective, particularly for small-scale recycling facilities or regions with limited resources. The limitations of NIRS-based classification highlight the need for alternative approaches that incorporate additional factors, such as the shape and form of plastics, to enhance accuracy and overcome the challenges associated with similar composition plastics.  
In light of these limitations, this paper proposes a novel plastic waste classification method that combines image sensors and object detection deep learning algorithms. By integrating shape-based analysis with compositional analysis, the proposed method aims to address the challenges posed by plastics with similar compositions but distinct physical attributes. The utilization of image sensors allows for the capture of visual information, enabling the classification of plastics based on their shape, texture, and other visual characteristics. Object detection deep learning algorithms provide the necessary computational framework for accurate and efficient classification on a conveyor belt system. The subsequent sections will delve into the details of the proposed method, including system design, operation principles, and experimental evaluations, to validate its effectiveness in improving plastic waste classification accuracy and recycling efficiency.