# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 1 Introduction and Background
### Sensor-Based Systems to AI: Advancing Waste Sorting
It evaluated more than twenty datasets and critiqued existing methodologies while suggesting future research paths [24]. However, the YOLO (You Only Look Once) deep learning model has emerged as one of the most widely adopted object detection frameworks, known for its exceptional speed and accuracy in real-time applications [25]. Ultralytics YOLOv8 [26] represents a significant advance, mainly due to extensive optimizations that have markedly improved the model's proficiency in object categorization [26]. These refinements make it particularly suitable for scenarios that require rapid and accurate decision-making, as commonly required in automated sorting systems. The latest iteration, YOLOv11, further advances these capabilities, providing state-of-the-art performance in a variety of applications, including detection, segmentation, pose estimation, and tracking. In waste sorting systems, the YOLO architecture can be trained to classify different categories of recyclables, such as plastic bottles, metal cans, and paper, even in situations where these objects are combined or displayed in chaotic or unstructured settings, enabling more efficient recycling processes. Several studies used the YOLO deep learning model for waste classification systems. [PERSON] et al. [27] enhanced plastic waste sorting by integrating image sensors with the YOLO deep learning model, achieving more than 91.7% accuracy in distinguishing plastics such as PET from PET-G. [PERSON] et al. [28] introduced a sorting approach that combines the YOLOX detection model with the DeepSORT tracking algorithm for rapid real-time plastic waste sorting. This approach leverages visual detection to increase sorting accuracy and efficiency, demonstrating considerable flexibility and potential in complex sorting scenarios. Several studies have demonstrated the potential of combining AI vision systems with PLCs to improve waste sorting efficiency. [PERSON] et al. [29] explored a vision-based recycling system controlled by a PLC, emphasizing the benefits of AI in the accurate detection of recyclable materials. Similarly, [PERSON] et al. [30] explored modern sorting techniques that combine AI with conventional sorting technologies. The aim was to improve the efficiency and effectiveness of MSW management. These studies suggest that AI and ML algorithms can significantly improve waste classification and segregation compared to traditional sensor-based systems. Multiple research efforts have explored the application of AI, spectrometry, and computer vision methods to enhance sorting processes. For example, [PERSON] et al. [31] introduced a PLC-based object sorting system that uses a combination of inductive, laser, and electromagnetic sensors to sort materials. However, while these methods are promising, they are still limited in handling unsegregated waste and complex material compositions. On the other hand, AI-powered vision systems can be trained to differentiate objects even in mixed, cluttered environments, making them more effective in real-world scenarios [32]. Investigations of AI-driven sorting solutions additionally emphasize the importance of scalability. Given that waste sorting facilities handle significant amounts of materials, achieving efficient scalability is crucial to sustaining high levels of operational throughput over time. Many studies, such as [33; 34; 35], have shown that AI models such as YOLO can process multiple objects in real time, offering scalability for industrial applications. This scalability, coupled with the ability to retrain models on new material classes, makes AI-driven sorting systems highly adaptable to future recycling needs. This paper presents an approach to designing a conveyor-based automated recycling sorting machine that integrates a PLC with an AI-driven vision system. Using the YOLOv8 deep learning model, the system can classify and sort paper, plastic, and metal cans with high precision.