# Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis
## 4 Discussion
### Testing Results  
In this section, the experiments use random twenty prohibitive sign images of varying sizes and environments for model checking. The accuracy and time measurements of the experiments are presented in Table 4.  
Generally, Yolo V3 SPP demonstrates higher precision than Yolo V3. The most leading average accuracy is Yolo V3 SPP 1 at 99.1%, followed by Yolo V3 SPP 3 at 93.33%. The trend is that the accuracy of Yolo V3 SPP grows along with the detection time. This indicates that Yolo V3 SPP needs more time to detect the sign. For example, for Yolo V3 SPP 1, the average time of detection is 0.458 s, and Yolo V3 1 needs 0.448 s. Further, a different scale affects the learning rate and detection time. The average detection time for Yolo V3 SPP 1 using scale = 0.1, 0.1 is 0.0458, falling 0.002 in Yolo V3 SPP 2 (scale = 0.2, 0.2) to 0.0456, while for Yolo V3 SPP 3 (scale = 0.3,0.3) detection time is 0.0455 s. These results indicate that if the system uses a large number for scale, the detection time will be faster. Hence, the accuracy decreases compare to the original scale in Yolo V3. Similar to this, the accuracy decreases in Yolo V3 SPP adopting a different scale. The experiment results thus show that Yolo V3 SPP is more robust than Yolo V3. In this work, we use three different scales and provided a deep analysis for Yolo V3 and Yolo V3 SPP. Based on this experiment result, we can summarize as follows. (1) If the system wants the highest accuracy, we can use the original scale = 0.1, 0.1. (2) The system will use scale = 0.3, 0.3 if we want to increase the detection time more quickly. The previous research [28; 31; 52] only focus on using the basic configuration of Yolo V3. They use the best weight provided from darknet with the scale = 0.1, 0.1. They optimize for accuracy but not for detection time. SPP contains more layers than the original method, which is why SPP takes more time for processing time. In our research, we provide a way to reduce detection time by increasing the scale parameter in the Yolo V3 and Yolo SPP configuration files. Our research proves that by using a scale = 0.3, 0.3 the detection time is faster than using a scale = 0.1, 0.1. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{2}{c}{**Yolo V3 1**} & \multicolumn{2}{c}{**Yolo V3 2**} & \multicolumn{2}{c}{**Yolo V3 3**} & \multicolumn{2}{c}{**Yolo V3 3**} & \multicolumn{2}{c}{**Yolo V3 3**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Acc**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**Time**} \\  & **(\%)** & **(s)** & **(\%)** & **(s)** & **(\%)** & **(s)** & **(\%)** & **(\%)** & **(s)** & **(\%)** & **(s)** & **(\%)** & **(\%)** & **(s)** \\ \hline
1 & 0.971 & 0.446  
Sub-sampling and max-pooling have significant benefits. Convolution subsampling can be stronger reversed in subsequent sampling layers. Max pooling works slightly for deleting certain maximum frequency noise from the target image by choosing only maximum values from adjacent areas. By merging them, SPP seems to utilize both benefits to improve Yolo V3's backbone network. Furthermore, Figure 5a-c gives the test results for the Yolo V3 model with an average accuracy of around 95.92% and a detection time of 0.4415 s. Moreover, Figure 5d-f shows the test results for Yolo V3 SPP using the similar image. The average accuracy is 97.27%, and the detection time is 0.4548 s. The system can identify the prohibitory sign class P3 well. In Figure 6a-c, Yolo V3 failed to detect all class P1 signs in the image, detecting only a single sign. However, Yolo V3 SPP 1 can detect three signs well in Figure 6d, and Yolo V3 SPP 2 can detect two signs in Figure 6e,f.