## 4 Results
### Description of External Environment and Practical Scenarios
#### 4.2.2 Experimental Evaluation and Experimental Results  
Because of the achievements of the simulations, they were molded using the same scenarios as in the case of the previous module, except that the detection method and the amount of information were different. According to the simulations, we obtain simultaneous detections on some elements of interest that are part of the category of those that offer safety in a traffic management system. We can say that visual tracking aims to deal with the streams of non-stationary images that change over time. Most existing algorithms have the ability to track objects in controlled environments but in areas with significant variations or fluctuating lighting they do not provide feedback as expected. This aspect is caused by the use of fixed analysis models without listing more details and characteristics, which limits the range of apparent detection. Detection based on incremental algorithms includes several components that correctly analyze the sample and add an ample modeling factor based on the basic observations. These adjustments demonstrate that there is the capacity for consistent improvements in the section dedicated to position, lighting, and area [81; 82].  
Therefore, we can see in Figure 9 that we have the possibility of identifying several objects at the same time, the information subsequently transmitted to the car interface or the traffic management system. The detection process is exemplary independently as a trigger, focusing on determining the transmission and, in turn, is maintained throughout the process. Therefore, once we get the shape of a class and it is detected according to the code sequence presented, a box aims to highlight each detected element and the specific message is ready to respond to the detected class, then sent to a prioritization list. According to experimental data, the classes mentioned in the detection process have a maximum interference time of between 55-120 milliseconds, p Being a network method for detecting objects in deep neural network images and video sequences, it discretizes the output space of boundary boxes in a set of default type boxes. These processes are on different scales, always using the location of the feature map [55]. When the prediction is made, the network provides results for each category of object that match the shape of the model. A graphical simulation illustrates the results obtained in the presented scenarios (Figure 10).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Video Duration** & **Scenario** & \begin{tabular}{c} **Frames** \\ **Detected** \\ \end{tabular} & \begin{tabular}{c} **Frames** \\ **Lost** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Objects** \\ \end{tabular} & \begin{tabular}{c} **Objects** \\ **Detected** \\ \end{tabular} & \
\begin{tabular}{c} **Travel Speed** \\ **km/h** \\ \end{tabular} &
\begin{tabular}{c} **Detection** \\ **Accuracy \%** \\ \end{tabular} \\ \hline
180 & 1 & 8964 & 12 & 196 & 146 & 15–20 & 74.40\% \\
220 & 2 & 7895 & 19 & 225 & 111 & 15–25 & 49.30\% \\
260 & 3 & 6785 & 27 & 328 & 184 & 15–20 & 56.10\% \\
180 & 4 & 8465 & 45 & 201 & 126 & 35–40 & 62.60\% \\
220 & 5 & 5683 & 32 & 333 & 176 & 30–40 & 52.80\% \\
280 & 6 & 6433 & 67 & 198 & 97 & 35–45 & 48.90\% \\
280 & 7 & 3453 & 146 & 444 & 104 & 60–65 & 23.40\% \\ \hline \end{tabular}
\end{table}
Table 2: Detection accuracy details, total blocks decoded, frames and objects detected from total identified elements existing in the image.  
Figure 9: Results with detection object for external environments, interference time, and percentages for detection.