# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 4 Experiments
### Experimental Analysis  
#### 4.2.1 Evaluations on CCTSDB  
To analyze the test results, we set a threshold \(t\), which is the class-specific confidence score. When the confidence scores of traffic signs were below the threshold \(t\), we filtered those candidate regions of traffic signs. We set \(t\) = 0.5 to ensure the high precision of detection. Table 3 shows the rates of precision (P) and recall (R) of our presented three models (A, B, C) and YOLOv2 on CCTSDB. Figure 6 shows the precision-recall curves of the three super categories. The precision is the ratio of true positives to the sum of true positives and false positives. The recall is the ratio of true positives to the sum of true positives and false negatives. The time is the average running time of each image processed. The AUC is the area under the curve of precision and recall. In addition, the best experimental results in all tables are bold.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**City Road** & **Highway** \\ \hline Varieties of traffic signs & Mainly focus on the prohibitory signs and danger signs \\ Influenced by trees and advertising signs easily & Hard to shelter due to the wide view \\ Color deterioration and distortion because of long-term usage & Bright color and good protection \\ Distributed densely, main focus on the intersection & Distributed sparsely \\ Complex background & Plain background \\ Slow vehicle speed for acquiring data & Fast vehicle speed for acquiring data \\ \hline \hline \end{tabular}
\end{table}
Table 2: The differences between city roads and highways.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Mandatory** & **Prohibitory** & **Danger** & **Time (s)** \\ \hline \multirow{3}{*}{YOLOv2} & P & **100\%** & 98.88\% & 95.92\% & \multirow{3}{*}{0.04} \\ \cline{2-4}  & R & 76.56\% & 70.48\% & 92.16\% & \\ \hline \multirow{3}{*}{Model-A} & P & 98.73\% & **99.61\%** & **99.26\%** & \\ \cline{2-4}  & R & 74.16\% & 68.62\% & 88.82\% & \\ \hline \multirow{3}{*}{Model-B} & P & 98.81\% & 99.27\% & 98.63\% & \multirow{3}{*}{**0.02**} \\ \cline{2-4}  & R & 79.43\% & **72.74\%** & **94.74\%** & \\ \cline{2-4}  & P & 91.89\% & 98.86\% & 98.59\% & \\ \cline{2-4}  & R & **81.34\%** & 69.41\% & 92.11\% & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of improved models in the CCTSDB (\(t\) = 0.5).  
From Table 3, we can see that the improved models were faster than the YOLOv2. Overall, Model-B performed better than others. Model-B was fastest; only 0.02 s per image. When \(t\) = 0.5, the recall rates of prohibitory signs and danger signs predicted by Model-B were the best. The precision rates of the three categories signs predicted by Model-B were the second. The higher recall rates indicated that there were a few traffic signs missed. The higher precision rates indicated that there were a few negatives predicted as traffic signs. The values of AUC in Figure 6 show that Model-B was more robust than Model-A. The values of AUC for mandatory signs and prohibitory signs in Model-A were slightly higher than those in Model-B, but the value of AUC for danger signs was significantly lower than others. The experimental results indicated that the multiple \(1\times 1\) convolutional layers in the intermediate layers and fewer convolutional layers in the top layers could increase the detection rate and reduce the time consumption. In contrast to CTSD, our Model-B detected more subclasses of traffic signs that were not marked. This shows the better generalization ability of Model-B. Compared to mandatory signs and danger signs, the recall rates of prohibitory signs were lower. This is mainly because prohibitory signs are distributed densely and their sizes are too small in some situations. Figure 7 shows some examples that are difficult to identify in traffic scenes.