## 4 Results and Discussion
### Evaluation of Temperature Detection and Fire Prevention System  
Five controlled experimental scenarios were designed to evaluate the system's effectiveness in detecting abnormal temperature rises and preventing fire-related incidents. These included variations in material composition, conveyor belt speed, and environmental conditions, such as dust. The primary goal was to determine the system's sensitivity, response speed, and reliability in triggering early alerts based on thermal camera data. Figure 16: Edge detection results: The left edge has not been detected. Figure 15: Edge detection results: Both edges detected. As summarized in Table 1, each test scenario recorded the average and maximum temperature readings and the system's response time. These values were measured from when the temperature exceeded a predefined safety threshold to the activation of local or remote alerts. The results indicate that, in scenarios involving iron--particularly under high-speed or dusty conditions--the temperature rose significantly, reaching a maximum of 45.3\({}^{\circ}\)C in Scenario 2. This suggests a heightened fire risk due to potential sparks or friction-generated heat. The system's response time remained fast, ranging from 1.7 to 2.5 s. This quick reaction window is crucial in halting processes, activating alarms, and reducing the likelihood of fire escalation. Figure 17 presents a comparative bar graph illustrating the average and peak temperatures across scenarios, along with the system's response time (blue line). The graph shows that scenarios with iron content resulted in higher temperatures and longer response times, while scenarios without iron maintained safe temperature ranges and faster system reactions. To further validate the performance under real-world industrial conditions, an additional test was conducted with a small iron object placed on the running conveyor belt of the BECs while the magnetic drum was active. The sequence of thermal frames captured during this test is shown in Figure 18. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Class** & **Images** & **Instances** & **Box (P)** & **Box (R)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **Mask (P)** \\ \hline All & 519 & 1105 & 0.868 & 0.823 & 0.914 & 0.679 & 0.878 \\ \hline Sharp & 237 & 461 & 0.864 & 0.837 & 0.908 & 0.706 & 0.882 \\ \hline Smooth & 176 & 644 & 0.873 & 0.809 & 0.921 & 0.652 & 0.873 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The final performance results of the YOLOv11n-seg model. Figure 17: Performance of the temperature detection system across five test scenarios, showing the average and maximum temperatures (bar graph) and system response time (blue line). As the iron particle approached the drum, it was magnetically trapped beneath the belt, resulting in a rapid and localized temperature increase. The thermal detection system successfully identified this anomaly, reaching over 40 \({}^{\circ}\)C, and it triggered alerts before any damage could occur. This scenario demonstrates the system's capability to detect dangerous temperature spikes in realistic operational environments. An aggregated analysis was conducted on all test cases to evaluate the reliability further. Out of the entire set of detection events, 97% were successfully identified and triggered the appropriate alert mechanisms, while only 3% failed--mainly due to momentary sensor latency or transitional errors. This overall success rate is illustrated in Figure 19, which visualizes the distribution of successful versus failed detections. The fire detection system demonstrated high accuracy, rapid response, and reliability in identifying early signs of hazardous conditions. The 97% success rate and sub-3-second reaction time validated the system's suitability for real-time industrial deployment. These findings support its role as a proactive safety mechanism within the BECS conveyor setup. Figure 19: Overall success rate of the fire detection system across all experimental scenarios.