# HelpResponder--System for the Security of First Responder Interventions
## 6 Discussion  
This section will discuss the main issues and differences with other solutions in the context of the research paper presented according to the results explained above. This work's objective was to provide frontline firefighters with an optimal fire rescue location strategy based on fire scene information (including ignition points, environmental parameters, and locations of firefighters). This information is obtained using network sensors and autonomous agents. Therefore, the firefighters can concentrate their efforts on the fire focus to quickly bring out the rest. This study is believed to innovate in location and monitoring firefighter interventions to improve methods in indoor operations. Table 8 shows a sum-up comparison of the main contributions in the field of the research described in this paper. In the works [9; 10; 11; 12; 13; 14], there is a lack of solution applied to indoor environments without visual information because of the smoke of fire and absence of GNSS signals. Our proposal's system predicts the origin of the flame, the fire focus, thanks to the models that have been integrated into the algorithm explained in Section 3. In contrast with [16], the experimental results indicate that the developed processing method on thermal images in false color RAINBOW is a suitable solution for the fast detection of fires with a high accuracy rate in indoor spaces with smoke and no vision. The system detect the location of the flame's focus thanks to the models of the algorithm explained in the previous section. Nevertheless, with the increasing advance in neural networks applied over image processing, it is possible to train a deep neural network that is capable of detecting fire [15] in thermal images with our developed method, which improves the processing time and reduces the false positives below 1% and the false negatives below 2%. Although most solutions usually use RGB cameras, we successfully validated our indoor spaces algorithms with no visibility. Our algorithm allows us to detect the beginning of the emergency's focus when the level of the fire is imperceptible using images with a high level of smoke. Moreover, most of the works make their proposed static \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Point**} & **Fireighter** & \multirow{2}{*}{**Position on** **the x-Axis (m)**} & \multirow{2}{*}{**Error (m)**} & \multirow{2}{*}{**Point**} & **Position on** **the x-Axis (m)** & **Position on** **the y-Axis (m)** & **Error (m)** \\ \hline
0 & 0 & 0 & 0 & 9 & 1.195 & 5.652 & 0.165 \\
1 & 0.548 & 0.582 & 0.012 & 10 & 1.420 & 6.420 & 0.050 \\
2 & 1.142 & 1.118 & 0.007 & 11 & 1.407 & 7.220 & 0.050 \\
3 & 1.570 & 1.794 & 0.273 & 12 & 1.369 & 8.019 & 0.100 \\
4 & 1.097 & 2.439 & 0.218 & 13 & 1.336 & 8.818 & 0.141 \\
5 & 0.701 & 3.134 & 0.256 & 14 & 1.302 & 9.617 & 0.133 \\
6 & 0.023 & 3.558 & 0.094 & 15 & 0.697 & 10.141 & 0.036 \\
7 & 0.423 & 4.251 & 0.330 & 16 & 0.093 & 10.666 & 0.195 \\
8 & 0.803 & 4.955 & 0.229 & 17 & \(-\)0.521 & 11.178 & 0.129 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The position of the point in the map followed the Beacons to the fire focus. and security cameras, making detection easier. However, in this work, we have deployed and evaluated the system using a moving camera. For this reason, our system has detection metrics lower than 90% because the moving camera complicates the results. Additionally, the focus of this type of camera is static, which has made deployment difficult. Moreover, we have deployed and evaluated the processing to answer in real time in an embedded system with computational limitations, optimizing the results to operate in the proposed scenarios. Related to sensor networks, the sensors previously deployed have certain advantages, such as knowledge of exact locations and the ability to provide data from regions that may be occluded due to closed doorways or structural collapse.