## 5 Experimental Results
### Experiments with Dataset  
#### 5.1.1 Experimental Environment and Details  
This experiment uses the Chinese traffic signs dataset, TT100K (Figure 10), which contains 100,000 traffic signs. The dataset contains 221 types of traffic signs, 6105 training sets, 3071 test sets, and 2048 \(\times\) 2048 picture sizes. As the largest and most comprehensive traffic signs dataset in China, TT100K not only covers the vast majority of scenes in China's actual driving environment but also different lighting and weather conditions. Owing to the imbalance in the number of different categories in the dataset, the category with number of instances lesser than 100 was excluded in the experiment, and 45 traffic signs with a large amount of data were selected for detection. These 45 traffic signs were as follows: WO, W13, W32, W55, W57, W59, P3, P5, P6, P10, P11, P12, P19, P23, P26, P27, PG, PH4, PH4.5, PH5, I2, I4, I5, IL100, IL60, IL80, IO, IP, PL120, PL30, PL40, PLS5, PL50, PL60, PL70, PL80, PL100, PM20, PM30, PM55, PN, PNE, PO, and PR40. In this study, data expansion was performed for the categories with less data in the 45 types of traffic signs. Two image preprocessing methods, light intensity changing, and random Gaussian noise were adopted. After expansion, the training and the test sets contained 15,684 and 4200 images, respectively. The dataset diagram is presented in Figure 11. The annotated file containing the position coordinates and categories of the detected object was an XML file, as depicted in Figure 12.  
Figure 9: DCN sampling process. (**a**) normal process; (**b**) deformable process; (**c**) widen process; (**d**) rotary process. All the experiments in this study used the same training platform. The processor of the platform is an Intel six-core 12-thread i7-8700 (Santa Clara, CA, USA), the graphics card comprises two Nvidia GTX2080 TI (Santa Clara, CA, USA), the video memory size is 11 GB, the memory size is 64 GB, and the operating system of the training platform is Ubuntu 18.04. Additionally, to prove the robustness of this method, the traffic signs in the dataset were divided into small (pixel range (0, 32], small), medium (pixel range (32, 96], middle), and large (pixel range (96, 400], large) categories for index evaluation according to the pixel range. The number of small, medium, and large targets in the test set are summarized in Table 1.  
The algorithm in this study adopts two evaluation indices for evaluation: mean average precision (mAP) and frames per second (FPS). In the evaluation index, mAP is the mean of the average precision (AP) of various traffic signs. FPS is an important index of detection speed, which reflects the performance of real-time detection algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Target Number** & **Proportion (\%)** \\ \hline Small target & 4226 & 44.93 \\ Medium target & 4412 & 46.90 \\ Large target & 768 & 8.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of targets. Figure 10: TT100K category. The detection result index of each category is precision, that is, the percentage of correctly detected traffic signs in all detected areas. It is calculated according to Equation (5). Recall, which is the percentage of traffic signs correctly detected in all traffic signs, is calculated according to Equation (6). A curve is drawn according to the accuracy and recall rates of the test results of each category. The area under the curve is the average accuracy, and it is calculated according to Equation (7). \[precision=\frac{TP}{TP+FP} \tag{5}\]  
\[recall=\frac{TP}{TP+FN} \tag{6}\]  
\[AP=\int_{0}^{1}p(r)\mathrm{d}r \tag{7}\]  
where \(TP\) denotes the number of correctly detected traffic signs; \(FP\) is the number of traffic signs detected in the background; \(FN\) is the number of traffic signs detected as the background; \(p\) is the precision on the longitudinal axis; and \(r\) is the recall on the horizontal axis.