## 5 Experiment and Analysis of Results
### Analysis of Experimental Data
#### 5.3.3 Comparative Analysis of Experiments in Different Scenarios  
In order to study the adaptability of the optimization model to multi-scenario and multi-weather traffic detections, 1000 sets of pictures were taken for each scene through the self-made datasets of sunny, cloudy, rainy, and night, then they were tested with YOLOv3 and YOLOv3-DL models. The experimental results are shown in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & \begin{tabular}{c} **Number of** \\ **Actual** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Statistics** \\ \end{tabular} & **Precision (\%)** & **Recall (\%)** &
\begin{tabular}{c} **Accuracy Rate** \\ **(\%)** \\ \end{tabular} \\ \hline Training set & 11,1351 & 11,2422 & 94.23 & 95.14 & 95.14 \\ \hline Training verification set & 139098 & 14,0427 & 94.24 & 95.14 & 95.14 \\ \hline Verification set & 27,747 & 27,982 & 94.36 & 95.16 & 95.16 \\ \hline Test set & 34,760 & 35,416 & 93.22 & 94.98 & 95.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: YOLOv3 general model traffic statistics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & \begin{tabular}{c} **Number of** \\ **Actual** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Statistics** \\ \end{tabular} & **Precision (\%)** & **Recall (\%)** &
\begin{tabular}{c} **Accuracy Rate** \\ **(\%)** \\ \end{tabular} \\ \hline Training set & 11,1351 & 11,3929 & 96.73 & 98.97 & 98.97 \\ \hline Training verification set & 13,9098 & 14,0937 & 96.77 & 98.05 & 98.97 \\ \hline Verification set & 27,747 & 28,374 & 96.92 & 99.11 & 99.11 \\ \hline Test set & 34,760 & 3,5870 & 95.82 & 98.88 & 98.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: YOLOv3-DL general model traffic statistics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & \begin{tabular}{c} **Number of** \\ **Actual** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Statistics** \\ \end{tabular} & **Precision (\%)** & **Recall (\%)** &
\begin{tabular}{c} **Accuracy Rate** \\ **(\%)** \\ \end{tabular} \\ \hline Training set & 11,1351 & 11,2422 & 94.23 & 95.14 & 95.14 \\ \hline Training verification set & 139098 & 14,0427 & 94.24 & 95.14 & 95.14 \\ \hline Verification set & 27,747 & 27,982 & 94.36 & 95.16 & 95.16 \\ \hline Test set & 34,760 & 35,416 & 93.22 & 94.98 & 95.01 \\ \hline \hline \end{tabUsing the DIOU-optimized network model YOLOv3-DL, we find that the statistical average accuracy is improved to 4.53% and the network performance has improved significantly. For different scenes and weathers, its adaptability has been improved as compared to that of YOLOv3, which can more accurately detect vehicles and be used for monitoring tasks of traffic flows. Based on the results of experimental tests on multiple scenes and weathers, some vehicles will be missed in the detections of YOLOv3 before optimization, which leads to a decline in statistical accuracy, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data Sets**} & **Number of Actual** & **Number of Statistics** & **Precision (\%)** & **Recall (\%)** & **Accuracy Rate (\%)** \\ \hline Sunny & 6823 & 6861 & 98.33 & 98.87 & 98.91 \\ \hline Cloudy & 5922 & 6031 & 98.17 & 99.99 & 99.97 \\ \hline Rainy & 8517 & 8694 & 97.59 & 99.61 & 99.56 \\ \hline Night & 5731 & 5852 & 97.88 & 99.95 & 99.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: YOLOv3-DL multi-environment traffic statistics.  
Figure 7: Comparison of Vehicle Detection Based on Optimized Models (**a**) YOLOv3 algorithm detection results and (**b**) YOLOv3-DL algorithm detection results.