# Simplified Routing Mechanism for Capsule Networks
## 6 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **[PERSON] et al. [11]** & **[PERSON]** & **[PERSON] [20]** \\ \hline Speed limit 20 km/h & **0.8667** & 0.8167 & 0.6500 \\ Speed limit 30 km/h & 0.9889 & **0.9917** & 0.9458 \\ Speed limit 50 km/h & **0.9920** & 0.9893 & 0.9707 \\ Speed limit 60 km/h & **0.9889** & 0.9756 & 0.9333 \\ Speed limit 70 km/h & **0.9758** & **0.9758** & 0.8727 \\ Speed limit 80 km/h & 0.9619 & **0.9810** & 0.8000 \\ Speed limit 100 km/h & **0.8533** & **0.8533** & 0.8067 \\ Speed limit 120 km/h & **0.9133** & 0.8822 & 0.8289 \\ End of speed limit (80 km/h) & 0.9444 & **0.9511** & 0.9000 \\ No passing & 0.9958 & **1.0000** & 0.9458 \\ No passing for vehicles over 3.5 metric t & 0.9924 & **0.9955** & 0.9697 \\ Right-of-way at the next intersection & 0.9619 & **0.9714** & 0.8929 \\ Priority road & 0.9812 & **0.9884** & 0.9087 \\ Yield & **0.9972** & 0.9972 & 0.9861 \\ Stop & **1.0000** & **1.0000** & 0.9630 \\ No vehicles & **1.0000** & **1.0000** & 0.9762 \\ Vehicles over 3.5 metric tons prohibited & **0.9933** & 0.9933 & 0.9533 \\ No entry & **0.9972** & 0.9972 & 0.8806 \\ General caution & 0.9385 & **0.9410** & 0.7231 \\ Dangerous curve to the left & **1.0000** & **1.0000** & 0.4833 \\ Dangerous curve to the right & **0.9889** & **0.9889** & 0.8889 \\ Double curve & **0.7667** & 0.7000 & 0.6778 \\ Bumpy road & 0.9833 & **0.9917** & 0.9083 \\ Slippery road & **0.9467** & 0.9267 & 0.6667 \\ Road narrows on the right & **0.9444** & **0.9444** & 0.5333 \\ Road work & **0.9542** & 0.9500 & 0.9438 \\ Traffic signals & **0.8278** & 0.8167 & 0.7833 \\ Pedestrians & 0.5000 & **0.5667** & 0.5000 \\ Children crossing & **0.9933** & **0.9933** & 0.9200 \\ Bicycles crossing & **1.0000** & **1.0000** & 0.9222 \\ Beware of ice/snow & 0.7667 & **0.7733** & 0.5000 \\ Wild animals crossing & **0.9815** & 0.9741 & 0.9074 \\ End of all speed and passing limits & **1.0000** & **1.0000** & **1.0000** \\ Turn right ahead & **0.9952** & **0.9952** & 0.9476 \\ Turn left ahead & **0.9917** & **0.9917** & 0.9833 \\ Ahead only & 0.9923 & **0.9974** & 0.9538 \\ Go straight or right & **0.9750** & 0.9667 & 0.9417 \\ Go straight or left & **1.0000** & **1.0000** & 0.9000 \\ Keep right & 0.9739 & **0.9870** & 0.9087 \\ Keep left & **1.0000** & **1.0000** & 0.8000 \\ Roundabout mandatory & 0.9667 & **0.9778** & 0.8000 \\ End of no passing & **0.8000** & 0.7667 & 0.5500 \\ End of no passing by vehicles over 3.5 t & **0.9778** & **0.9778** & **0.9778** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification test accuracy by class for capsule networks on the GTSRB dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **[PERSON] et al. [11]** & **[PERSON]** & **[PERSON] [20]** \\ \hline
0 & **0.9151** & 0.8928 & 0.9002 \\
1 & **0.9686** & 0.9547 & 0.9280 \\
2 & 0.9393 & **0.9549** & 0.9152 \\
3 & **0.8636** & 0.8550 & 0.7696 \\
4 & 0.9080 & **0.9164** & 0.9045 \\
5 & 0.8624 & **0.8968** & 0.8335 \\
6 & 0.8402 & **0.8720** & 0.8255 \\
7 & 0.8579 & 0.8742 & **0.8757** \\
8 & 0.7861 & **0.8193** & 0.7657 \\
9 & 0.8245 & **0.8621** & 0.8301 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification test accuracy by class for capsule networks on the SVHN dataset. effectiveness on six different datasets, compared with neural network-based solutions and capsule-based solutions. As can be seen, the training time was reduced in all cases, by almost 30% on average. Even in the worst case, a speed increase of almost 20% was achieved. And in some cases, an increase in speed of almost 50% can be seen. Despite the increase in speed, the efficiency of the network has not decreased. For several different metrics, our proposed solution was compared with other capsule-based methods. As we have shown, our proposed approach can increase the efficiency of the routing mechanism. For all six datasets tested in this research, our solution provided the highest results in almost all cases.