# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 3 Results
### Performance Evaluation of URA-VLMs
#### 3.3.2 Real-Time Performance  
The real-time tests aim to conduct a basic assessment of the situations that the model may encounter in practical applications under conditions that closely mimic real-life scenarios. Here, we evaluate the response time and the performance of the model in complex environments.  
* Response time  
Firstly, we conducted tests using the InterVL2.5 27B model on a cloud server and the InterVL2.5 7B model on the edge. The time taken for each step is presented in the following table. On cloud servers, the average time for the entire inference process was 9.3 s, whereas on the edge, it was 22.6 s. Additionally, for edge cloud computing, the average time for the entire inference process was recorded as 12.1 s (Table 12).  
* Robustness under diverse environmental conditions  
The road anomalies monitoring methods are highly susceptible to the influence of extreme weather [60, 61]. To assess robustness under diverse environmental conditions, we conducted basic qualitative tests. Our findings indicate that the time of day--daytime versus nighttime--does not significantly affect the model's judgment, provided that the human annotator can accurately assess the situation.  
However, adverse weather conditions such as strong winds and rain do impact the model's judgment. In some cases, despite extreme weather conditions, the collection of video and images remains unaffected, allowing for the acquisition of clear images for inference, thereby not influencing the model's judgment. Conversely, heavy storms or rains  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**No.**} & \multirow{3}{*}{**Safety Level**} & \multicolumn{2}{c}{**Number of Data Points**} & \multicolumn{2}{c}{**Safety Level Accuracy (\%)**} \\ \cline{3-5}  & & **Points** & **InterVL-2.5 MPO (Preliminary Classification Prompt)** & **InterVL-2.5 MPO (Optimized Prompt)** \\ \hline
1 & GREEN & 714 & 91.81 & 92.42 \\
2 & LIGHT GREEN & 844 & 79.86 & 88.19 \\
3 & YELLOW & 1476 & 95.7 & 96.45 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance evaluation of URA-VLMs safety level before and after optimization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**No.**} & \multirow{3}{*}{**Tasks**} & \multicolumn{2}{c}{**(1)**} & \multicolumn{2}{c}{**Edge Computing**} & \multicolumn{2}{c}{**(2)**} \\  & & **Cloud Computing Response Time (ms)** & **Response Time (ms)** & **Edge Cloud Computing** \\  & & **InterVL2.5 26B** & **InterVL2.5 7B** & **Response Time (ms)** \\ \hline
1 & Road anomaly classification & 2656.59 & 5450.09 & 5450.09 \\
2 & Road waterlogging depth estimation & 2884.02 & 8319.64 & 2884.02 \\
3 & Road safety level & 3763.04 & 8903.92 & 3763.04 \\  & Total & 9303.65 & 22,673.66 & 12,097.15 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The response times of the servers.  
can hinder data collection, as car window glasses may become contaminated by water stains, resulting in compromised data quality that adversely affects the model's judgment.  
In conclusion, due to extreme weather conditions, it becomes difficult to identify images. For example, large areas of flare and light pollution not only make it challenging for the model to make judgments but also for humans. This affects the results and tends to cause illusions in the model, making it hard to accurately describe the images. However, changes in lens angles and small areas of image blurring do not significantly impact the model's judgment, and it can still perform normal reasoning.