## 6 Results
### Fusion Performance with Security Module  
The training of the ResNet50 network (security module backbone) is based on a fine-tuning process. That is, the weights trained in ImageNet [28] were used and tuned along the training epochs in all layers of the network. After this process, the final ResNet50 configuration was applied as a binary output person detector. The ResNet50 network was trained for 500 epochs using the cross-binary entropy cost function. The network accuracy to detect people in the scenery in the test set was 93.96%.  
Figure 13 (Percentage Accuracy \(\times\) Epochs) and Figure 14 (Loss value \(\times\) Epochs) show the results referring to training via supervised learning of the security module: the result referring to the evolution of the accuracy curve in the training set and the reduction of the cost function, respectively.  
Figure 12: Mission trajectory with the Late Fusion–DDQN method.  
Figure 13: Evolution of the accuracy in the training set.  
The accuracy in Figure 13 is presented in terms of proportion, where the value 1 represents a perfect 100% accuracy. The loss value in Figure 14 represents the direct value calculated by the dimensionless cross-entropy function.  
After completing the supervised learning training, the safe detection algorithm was inserted into the mobile robot and started to work together with the sensor fusion method in a separate module, or complementary. Table 3 presents the results referring to the tests developed in the simulation environment. The tests were performed with the robotic agent exclusively navigating with the fusion algorithm and with the help of the security module. To increase the complexity of the environment, people who move in the scene were also inserted.  
Three sets of tests were included with the robot in the dynamic environment, where three people were inserted who moved randomly. The average success rate in the proposed mission was 46.7%. Without the security module, the success rate was 33.3%. It is evident that the security module avoided the robot's collision at certain times and increased the average of final rewards: 6.51% without the security module and 9.37% with the security module. Collisions continued to occur because people in the scene could collide with the side or rear of the robot, out of its field of vision, due to the randomness and unconsciousness of their movements.  
Figure 14: Minimization of the cost function in training.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{
\begin{tabular}{c} **Test** \\ **Number** \\ \end{tabular} } & \multicolumn{2}{c}{**Late Fusion (LF)**} & \multicolumn{2}{c}{**LF + Security Module**} \\ \hline \multirow{3}{*}{First} & Reward Average & 6.22 & Reward Average & 8.83 \\ \cline{2-5}  & Success Rate & 40\% & Success Rate & 40\% \\ \hline \multirow{3}{*}{Second} & Reward Average & 7.61 & Reward Average & 9.08 \\ \cline{2-5}  & Success Rate & 40\% & Success Rate & 60\% \\ \hline \multirow{3}{*}{Third} & Reward Average & 5.72 & Reward Average & 10.19 \\ \cline{2-5}  & Success Rate & 20\% & Success Rate & 40\% \\ \hline Final Average & & 6.51 & & 9.37 \\ \hline \multicolumn{5}{l}{Success Rate on} & \multicolumn{2}{c}{33.3\%} & \multicolumn{2}{c}{46.7\%} \\ \cline{2-5} Tests & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metrics measured in the deployment process in a dynamic environment.