## 1 Introduction  
Metal recycling is crucial in industrial waste management and sustainable development by minimizing environmental pollution and conserving natural resources [1]. Efficientseparation of non-ferrous metals, such as aluminum and copper, from mixed waste is essential for maintaining high recycling quality and economic viability. One widely used method is the barrier eddy current separator (BECS), which relies on electromagnetic induction to create repelling forces between non-ferrous metals and non-conductive materials, like plastic and wood [2; 3; 4; 5; 6; 7]. In BECS systems, the conveyor belt not only transports materials, but also influences separation accuracy, as even minor disruptions can affect the material trajectory and interaction with the magnetic drum.  
Industrial conveyor belts are exposed to multiple challenges, including the presence of sharp-edged or irregular materials, which typically result from malfunctioning upstream processes, such as milling [8; 9; 10]. These sharp fragments can damage belt surfaces, reduce separation accuracy, and pose hazards during handling and maintenance. Additionally, irregular material shapes disrupt magnetic field uniformity, lowering eddy current efficiency [11]. Therefore, the early identification of such anomalies helps optimize system performance and enhances workplace safety. Figure 1 illustrates the internal structure of the milling machine used in the preprocessing stage to grind and round metal particles. Its internal blades are designed to eliminate sharp edges, ensuring smoother fragments are conveyed to the eddy current separator.  
This study presents an intelligent monitoring and protection system for BECS conveyor belts, utilizing image processing and artificial intelligence (AI) to address the challenges above. Integrating AI into safety-critical systems reflects a growing trend in recent research, such as developing AI-based workbenches for testing methodologies in occupational health and safety contexts [12].  
Figure 1: Internal view of the industrial milling machine that is used in the upstream process. The image shows the rotating blades and grooved chamber responsible for grinding and smoothing sharp-edged metal fragments before they enter the eddy current separator. The presence of sharp particles on the conveyor belt may indicate a malfunction or wear in this unit, highlighting its relevance to the proposed monitoring system.  
The proposed system includes a thermal camera for detecting abnormal heat spots and preventing fire hazards, a low-power image processing unit for automatic belt misalignment correction, and a line-scan camera integrated with the YOLOv11 model [13] to evaluate the quality of materials transported on the belt. The system aims to protect industrial equipment and human health by reducing manual intervention and enabling the early detection of risks [14].  
The aim of this research was to design and implement an intelligent, multi-module monitoring system that improves the safety, efficiency, and separation performance of conveyor belts in BECS systems.  
The specific objectives of this study were as follows:  
* To detect and classify material shapes (sharp vs. smooth) using a YOLOv11-based vision system to identify upstream process anomalies and reduce conveyor damage.
* To design and test a misalignment detection and correction module using image processing and stepper motors to maintain accurate belt alignment.
* To implement a thermal monitoring module using an MLX90640 thermal camera to detect the abnormal temperature rises caused by trapped ferrous materials.
* To integrate an IoT-based alert system for notification and remote response to safety hazards.