## 3 Proposed Plastic Waste Classification Method
### Optical System Design  
The optical system design (Figure 3) involves the selection of a depth camera and the construction of a plastic waste classification system. The depth camera (D455, Intel, Santa Clara, CA, USA) was selected based on criteria such as depth resolution, shutter type, pixel size, and frame rate. The D455 depth camera features a pixel size of 3 \(\upmu\)m \(\times\) 3 \(\upmu\)m and is capable of measuring objects even at a distance of approximately 20 m. With a frame rate of 90 fps, it is well-suited for capturing images of plastics on a high-speed conveyor belt. An optical system for morphology-based plastic waste classification was devised by integrating three machine/computer vision cameras into a multi-camera module. The system is designed to ensure that each of the three image sensors can capture data from a 1 m distance. To mitigate the effects of real-world factors such as dust and vibrations, the system was positioned at a height of 1 m above the conveyor belt. Furthermore, an optical setup capable of accommodating the practical sorting environment was constructed, enabling coverage of the 3-meter width of the conveyor belt. This setup allows for comprehensive coverage and accurate measurement of the plastics on the conveyor belt. To classify the plastic waste, air nozzles are installed at the end of the conveyor belt. These air nozzles play a crucial role in the classification process by using pneumatic control. They are strategically placed to correspond with specific regions or samples on the conveyor belt. The machine/computer vision data are divided and matched to the number and position of the air nozzles, enabling communication and control through algorithms. This ensures that the appropriate air nozzle is activated based on the classification results. Based on the selection of key modules and the design of the optical system, the system was built to acquire data in real-world scenarios. The lighting conditions were determined through tests to ensure the measurement of objects on the fast-moving conveyor belt, and the exposure function of the RGB camera was utilized for exposure control. As you can see in Figure 4, the sharpness of the image can be adjusted by controlling the exposure time. In Figure 4a of the PET bottle, the image was obtained with an exposure time of 10 ms, while Figure 4b was obtained with an exposure time of 5 ms. It can be observed that the image with a 5 ms exposure time is sharper. Additionally, as the exposure time decreases, the overall brightness of the image changes. Therefore, the brightness was adjusted by controlling the intensity of the lighting and applied to the classification system. Figure 3: Three camera installation layout for classification of PET and PET-G. Proposed Waste Classification System with Development of Deep Learning Classification Algorithm Based on Field Data  
The proposed waste classification system aims to develop a deep learning classification algorithm based on field data. Real-world plastic waste classification data is acquired from the conveyor belt (Figure 5a), which moves at a speed of 2 m per s. These data are essential for training and developing an accurate deep learning algorithm specifically tailored for the classification of PET and PET-G plastics in Figure 5b. The deep learning algorithm is trained on the augmented dataset (Figure 6), consisting of a wide range of variations in plastic waste samples. The algorithm learns to recognize and classify different types of plastics based on their visual features, patterns, and characteristics. The training process involves optimizing the network's parameters, adjusting the model architecture, and fine-tuning the algorithm to achieve high accuracy and reliable classification results. The proposed waste classification system combines the developed deep learning algorithm with the optical system to create an integrated solution. The real-time data captured  
Figure 4: PET image according to exposure: (**a**) 10 ms exposure time and (**b**) 5 ms exposure time.