# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 3 Results
### Azure Content Safety Evaluation in Advanced Traffic Navigation System  
This subsection details the evaluation of image content safety using Azure's Content Safety service, focusing on three critical categories such as self-harm, sexual assault, and  
Figure 5: \(GRQlk\) variation with increasing: (**a**) travel time; (**b**) road safety; (**c**) road quality; (**d**) traffic delays.  
violence. The assessment includes a confusion matrix for each category and calculates performance metrics to determine the effectiveness of the implemented solution.  
A dataset comprising 300 real-life images was employed to test the efficacy of Azure's Content Safety API within traffic navigation. The dataset was evenly distributed across three categories:  
* Self-harm: 100 images;
* Sexual assault: 100 images;
* Violence: 100 images.  
Each image was analyzed to determine its classification into the above categories. The API provides a binary classification (TRUE or FALSE) indicating the presence or absence of content for each category. Confusion matrices and performance metrics (Accuracy, Precision, Recall, and F\({}_{1}\)-Score), presented in Table 2, were constructed for each category to evaluate the classification performance based on the API's predictions versus the expected outcomes.  
The self-harm model demonstrates high precision (100%), indicating that it is always correct when predicting an image containing self-harm content. However, the recall is moderate (71.4%), suggesting that 28.6% of actual self-harm images were not detected. This imbalance implies that while false positives are non-existent, false negatives are a concern, potentially allowing some inappropriate content to bypass the safety filters.  
The sexual assault model performs well in identifying sexual assault content, with a precision of 75% and a recall of 50%. The relatively high precision minimizes the occurrence of false positives, while the recall indicates a reasonable detection rate, though there is room for improvement to reduce missed detections.  
The lowest performance metrics are observed in the violence category, with a precision of 100% and a recall of 37.5%. This indicates a significant number of false negatives where violent content is not detected, posing a risk in scenarios where safety is critical. Additionally, the 100% precision suggests that a violent prediction may be incorrect, leading to potential disruptions in system performance due to false alarms.  
While Azure's Content Safety API demonstrates strong precision in detecting self-harm and violence, the varying recall rates across categories indicate a need for further optimization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Confusion Matrix** & **Metrics** \\ \hline \multirow{4}{*}{Self-harm} & TP = 50 & Accuracy = 80\% \\  & FP = 0 & Precision = 100\% \\  & FN = 30 & Recall = 71.4\% \\  & TN = 20 & F\({}_{1}\)-Score = 83.3\% \\ \hline \multirow{4}{*}{Sexual assault} & TP = 30 & Accuracy = 60\% \\  & FP = 10 & Precision = 75\% \\  & FN = 30 & Recall = 50\% \\  & TN = 30 & F\({}_{1}\)-Score = 60\% \\ \hline \multirow{4}{*}{Violence} & TP = 30 & Accuracy = 50\% \\  & FP = 0 & Precision = 100\% \\ \cline{1-1}  & FN = 50 & Recall = 37.5\% \\ \cline{1-1}  & TN = 20 & F\({}_{1}\)-Score = 60\% \\ \hline \hline \end{tabular} Note: TP—True positive; FP—False positive; FN—False negative; TN—True negative.  
\end{table}
Table 2: Performance metrics.