# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 5 Experimental Results
### Exploring an Optimal Architecture--CarNet, by Making Experiments on PKLot Dataset
In Table 6, the testing scores for CarNet (with two convolutional layers), CarNet (with three convolutional layers), and CarNet (with four convolutional layers) are provided to demonstrate the effect of number of layers. The scores for CarNet (with two convolutional layers) and CarNet (with three convolutional layers) are higher than those for CarNet (with four convolutional layers). This shows that the use of fewer layers is more suited to our task. However, we determined that the optimal number of convolutional layers is three rather than two because the model with three convolutional layers yielded higher scores. We previously claimed that the use of a large window size in convolutional layers helps to solve our classification task efficiently. However, we do not know exactly how large should kernel size be. We proved that the kernel size that we chose is the best option for solving the current challenge. The comparison results are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name of Method**} & \multirow{2}{*}{**Name of Training Subset**} & \multicolumn{3}{c}{**Testing Accuracy**} \\ \cline{3-5}  & & **PUCPR** & **UFPR04** & **UFPR05** \\ \hline \multirow{3}{*}{CarNet (with two layers)} & PUCPR & 95.60\% & 94.20\% & 92.20\% \\  & UFPR04 & 96.10\% & 95\% & 90.30\% \\  & UFPR05 & 95.70\% & 95.10\% & 91.70\% \\ \hline \multirow{3}{*}{CarNet (with three layers)} & PUCPR & **98.80\%** & **94.40\%** & **97.70\%** \\  & UFPR04 & **98.30\%** & **95.60\%** & **97.60\%** \\  & UFPR05 & **98.40\%** & **95.20\%** & **97.50\%** \\ \hline \multirow{3}{*}{CarNet (with four layers)} & PUCPR & 62.80\% & 89.20\% & 68.40\% \\  & UFPR04 & 57.90\% & 88\% & 65.30\% \\ \cline{1-1}  & UFPR05 & 63.40\% & 87.50\% & 66.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of testing accuracies for Carnet (with two layers), CarNet (with three layers) and CarNet (with four layers). Figure 11: Comparison of CarNet (with two layers), CarNet (with three layers) and CarNet (with four layers). x-direction: number of epochs (500), y-direction: accuracy. (**a**) training accuracy; (**b**) validation accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Method** & **Training Accuracy** & **Validation Accuracy** \\ \hline CarNet (with two convolutional layers) & 96.82\% & 90.02\% \\ CarNet (with three convolutional layers) & **97.23\%** & **90.70\%** \\ CarNet (with four convolutional layers) & 97.06\% & 83.88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of training and validation accuracies for CarNet (with two layers), CarNet (with three layers) and CarNet (with four layers). The figure indicates that we selected the appropriate window size for solving the current problem. The exact training and validation accuracies for this experiment is provided by Table 7.  
We started our experiments training with a model which uses \(3\times 3\) kernel size and continued until experimenting a model which uses \(13\times 13\) kernel size. The lines representing the accuracy for window sizes 3 and 5 show the lowest training accuracy as well as the lowest validation scores. Meanwhile, the charts for the performance of CarNet (window size = 7) indicate average performances compared to all the other results, in both diagrams, Figure 10a,b. The scores for models which use 9, 11, 13 window sizes are comparable the highest ones in each diagram showing that using larger window sizes is more effective. The testing scores are given in Table 8. The testing results for CarNet (with window size = 3), CarNet (with window size = 5), CarNet (with window size = 7), CarNet (with window size = 9), CarNet (with window size = 11), CarNet (with window size = 13) are provided by the table. It is difficult to differentiate the performances clearly. However, overall it can be seen that the accuracies for models with larger window sizes indicate that these models are more robust.