## 5 Discussion
### Further Improvements and Research Directions  
To further enhance the plastic waste classification system, several improvements and research directions can be explored. Firstly, expanding the range of detectable plastics beyond PET and PET-G can increase the system's applicability in diverse waste management scenarios. Training the algorithm on additional plastic types and incorporating them into the system can improve its versatility and effectiveness. Moreover, incorporating advanced machine learning techniques, such as deep neural networks or reinforcement learning, may further enhance the system's classification accuracy and robustness. These techniques can capture more complex features and patterns, enabling the system to handle challenging scenarios and improve overall performance. Additionally, integrating sensor fusion techniques, such as combining depth cameras with other types of sensors (e.g., spectroscopic sensors), can provide complementary information about plastic waste materials. This fusion of data from multiple sensors can improve classification accuracy by capturing additional material properties and characteristics. The proposed method involves providing signals to the controller of the pneumatic nozzle in the area where inference is performed. While communication has been implemented, actual testing of the accuracy when classifying PET and PET-G by connecting to the pneumatic sensor is required. An important consideration here is the synchronization issue between the object detection inference results and the pneumatic valve, which is influenced by the conveyor speed. The delay in opening the pneumatic nozzle will vary depending on the conveyor speed. For example, on-site inference results can be calibrated to spray the pneumatic nozzle 100 ms after 1 m/s of conveyor speed, but when the conveyor speed changes to 2 m/s or 3 m/s, the delay time needs to be recalibrated on-site, and the results need to be organized for easy application on-site. Due to these differences, the accuracy of PET and PET-G classification may vary, necessitating future work to find a function for delay time based on conveyor speed to obtain reliable results. The current system is in the proof-of-concept stage and is being validated at a recycling site, with the challenge of achieving significant accuracy improvements in plastic waste sorting over an extended period. This paper presents an innovative method that combines image sensors and deep learning (YOLO model) to classify plastics by shape and form, aiming to improve sorting accuracy, reduce misclassification, and enhance recycling efficiency, addressing environmental concerns related to plastic waste mismanagement. The current system is in the proof-of-concept stage and is being validated at a recycling site. Achieving significant improvements in accuracy within a short period is challenging and requires extended validation over an extended period. Furthermore, continuous evaluation and optimization of the system's performance through field trials and feedback from waste management facilities are essential. Collaborating with industry partners and incorporating their expertise can help identify real-world challenges and refine the system accordingly. The development of machine vision technology promises to significantly accelerate the automation of plastic classification, which still heavily relies on complex systems or manual labor in many aspects. In addition to PET and PET-G, PET and PVC, despite their similar chemical compositions, must also undergo classification. Currently, differences in chlorine content are determined through methods involving equipment such as X-rays or manual sorting. However, it is anticipated that machine vision, with its capability to distinguish the distinct shapes of PET and PVC, will enable a more straightforward classification process.