## 3 Proposed Plastic Waste Classification Method
### Interface of the Field Classification Module  
As depicted in Figure 8, the Realsense camera module streams images to a PC via the USB3 interface, where deep learning inference is performed. The resulting inferences from the streamed images are transmitted as word data to the Holding Registers of Modbus TCP. In the case of PET, a value of 1 is sent. The transmitted results are linked to the PLC's Digital Out module to generate a signal that activates the air nozzle. When a PET image is detected, as illustrated in Figure 9a, a digital signal shifts from 0 to 1, analogous to a yellow signal on an oscilloscope. The corresponding change in the Holding Registers' value can be observed in the red-boxed value in Figure 9b. When the data are acquired in real-time from the camera via the USB3 interface to the  
Figure 7: PET and PET-G datasets with annotation. edge PC, the inference process starts using the internal YOLO-v8 algorithm. The inferred results (Figure 9a) are then communicated to the air nozzles located at the end of the conveyor belt through the Modbus TCP ( Figure 9b) communication interface. Based on the object detection inference results, which include the bounding box coordinates and class labels, the system determines the specific regions or samples on the conveyor belt that require classification and sorting. To achieve this, the system sends an \"O\" signal to the pneumatic valves connected to the corresponding air nozzle controller. For PET and PET-G classification, the air nozzles are divided into 64 regions, and, based on the inference results, the corresponding regions are activated by sending an \"O\" signal to the pneumatic valves. Only the samples in the activated regions are classified through pneumatic control. The activation of the appropriate air nozzles directs focused bursts of air toward the classified plastic waste samples, effectively sorting them pneumatically. This precise and automated process ensures that each plastic waste object is correctly categorized and directed to its designated location for further processing or recycling. While compositional analysis provides valuable insights into the material composition of plastic waste, it may not be adequate for distinguishing plastics with similar compositions but different shapes. To overcome this challenge, a shape-based classification algorithm is developed and integrated into the system. Figure 8: Schematic diagram of the proposed methodology. Figure 9: Interface with PLC from inference results: (**a**) It divides the image area into 64 areas and converts them into digital signals, and (**b**) checks the communication to the PLC using communication with ModbusTCP. The shape-based classification algorithm focuses on the visual characteristics, geometric features, contours, and textures of the plastic waste samples. By extracting relevant shape descriptors such as aspect ratios, circularity, convexity, and texture patterns, the algorithm can differentiate between different types of plastics based on their distinct shapes. The algorithm leverages the visual information captured by the image sensors to perform shape-based analysis. It analyzes the unique visual properties of each plastic waste sample and compares them against predefined patterns and shape templates to determine the appropriate classification. By incorporating shape information alongside the compositional analysis, the shape-based classification algorithm enhances the accuracy and reliability of the plastic waste classification system. The integration of both algorithms allows for a more comprehensive analysis of plastic waste, enabling precise classification, even in cases where compositional similarities exist. The combined approach of compositional analysis and shape-based classification significantly improves the system's ability to correctly identify and categorize plastic waste. It takes advantage of the strengths of each analysis method, mitigating the limitations of individual approaches and enhancing the overall effectiveness of the waste classification system.