## 4 Results and Discussion
### Prototyping and System Implementation  
Due to operational constraints and safety regulations, executing the proposed system directly on the full-size BECS was not feasible. The machine was shared with other research projects, making extended experimentation impractical. To overcome this, a scaled-down prototype of the BECS conveyor belt was constructed to test and validate the proposed systems: the conveyor misalignment detection and correction, thermal hazard detection, and material shape classification.  
The primary goal was to automate the manual correction of the conveyor belt misalignment. In the original BECS, two mechanical levers were used by technicians to manually re-align the belt after it shifts due to load imbalance, prolonged operation, or physical displacement. These levers are visible in the scaled prototype in Figure 24, where two stepper motors were installed to replace manual adjustments.  
The mini conveyor belt system replicated the original BECS structure using a lightweight aluminum profile framing and a custom plastic belt. The vertical camera mount was used for visual line detection, and a white band along the belt's edge helped to detect the left and proper boundaries. A compact but powerful stepper motor was added for main belt motion, and Raspberry Pi microcontrollers were used for both belt movement and misalignment correction control. The total cost of this modular test setup was approximately EUR 350.  
An industrial-grade line-scan camera was installed on the real BECS conveyor system to detect the material shapes on the belt. As shown in Figure 25, the camera was mounted on a rigid perforated metal bracket to allow flexible adjustment and alignment. The optical assembly included adjustable focus and aperture rings to fine tune the image capture based on conveyor speed and lighting conditions. Uniform illumination was provided by LED spotlights mounted on either side.  
The complete implementation setup, as shown in Figure 25, demonstrates all of the integrated components across the conveyor system. On the left, the line-scan camera setup captured high-resolution grayscale images to be analyzed by a YOLOv11n-seg segmentation model. At the center, an MLX90640 thermal camera monitors the critical region near the magnetic drum--where ferromagnetic materials may cause friction and heat buildup. The yellow box in the image highlights the MLX90640 thermal camera used in the setup. The yellow arrow points directly to this camera to distinguish it from the two other cameras (Raspberry Pi camera and the ELP camera). This camera captures thermal data used in our fire detection system.  
A floodlight ensures constant illumination for accurate thermal readings. On the right, a Raspberry Pi 4 manages all of the sensors and components, including the thermal camera, MQ-135 smoke detector [87], and KY-006 buzzer.  
If a rapid temperature increase or smoke is detected, the system triggers a buzzer alert and sends notifications--including thermal images--via Telegram. This dual-layer alerting system ensures technicians are informed immediately, even remotely. The total cost of the thermal and fire-detection unit, including all sensors and processing modules, is approximately EUR 180.  
The aluminum profile frame provides structural integrity and modularity, allowing rapid prototyping and reconfiguration. These cost-effective implementations allow complete testing without interfering with the original BECS machine while preserving high simulation accuracy. The successful operation of this scaled and modular system validates the feasibility and industrial applicability of the proposed intelligent automation methods.  
Figure 24: The scaled-down prototype of the BECS conveyor belt. Stepper motors were used to replace the manual correction levers, and a vertical camera was mounted for misalignment detection.