# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 5 Experimental Results
### Exploring an Optimal Architecture--CarNet, by Making Experiments on PKLot Dataset
See Table 3.  
As shown in the line graphs in Figure 9, initially the model without dilated convolutional layers shows a rapid increase in accuracy. However, in the final epochs the trend for CarNet (with dilated convolutional layers) shows a slight improvement over CarNet (without dilated convolutional layers. Table 4 shows the testing accuracies related to Figure 9. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Method** & **Training Accuracy** & **Validation Accuracy** \\ \hline CarNet (without dilated convolutional layers) & 96.47\% & 89.99\% \\ CarNet (with dilated convolutional layers) & **97.23\%** & **90.70\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of training and validation accuracies for CarNet (with dilated convolutional layers) verses CarNet (without dilated convolutional layers). Figure 9: Comparison of CarNet (with dilated convolutional layers) and CarNet (with normal convolutional layers). x-direction: number of epochs (500), y-direction: accuracy. (**a**) training accuracy; (**b**) validation accuracy. The table indicates that CarNet is dominant in all cases, showing the highest scores. A noteworthy fact is that results for CarNet (without a dilation) shows a quite good result. This explains the importance of applying the two other contributions of this study, that is, a large window size and a small number of layers, in that they have a significant influence when the current issue is addressed. Thus, the model shows high performance even when dilated convolutions are not used. Furthermore, the use of a small number of layers is an additional main contribution of our study, as mentioned previously. Thus, we conducted an experiment on models with varying numbers of convolutional layers to find the model that optimally fits the current task. Figure 11 illustrates a comparison of the scores for the training and validation sessions of CarNet (with two convolutional layers), CarNet (with three convolutional layers) and CarNet (with four convolutional layers). At a glance, one cannot easily determine which method shows the highest performance, especially when comparing CarNet (with two convolutional layers) and CarNet (with four convolutional layers). As seen in Figure 11, the line for CarNet (with three convolutional layers) shows a comparable best performance in (a) graph, demonstrating the highest training accuracy as well as the highest validation accuracy in (b) graph. With very small difference, the line for CarNet (with two convolutional layers) have the second highest scores. This proves that using less layers might be helpful for this task. Meanwhile, the lowest performance belongs to CarNet (with four convolutional layers. The training and validation scores are respectively 96.82% and 90.12% for CarNet (with two convolutional layers), while the scores for CarNet (with four convolution layers) are respectively 97.06% and 83.88%. See Table 5. Our proposed method contains three convolutional layers, for which we demonstrated the training and validation accuracy above. \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 a dilation)} & 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 (without a dilation)} & PUCPR & 94.70\% & 94.30\% & 93.80\% \\  & UFPR04 & 95.60\% & 94.50\% & 92.30\% \\ \cline{1-1}  & UFPR05 & 95.80\% & 94.90\% & 94.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of testing accuracies for Carnet (with a dilation) versus CarNet (without a dilation). Figure 10: Experimenting different window sizes to build an optimal model. x-direction: number of epochs (500), y-direction: accuracy. (**a**) training accuracy; (**b**) validation accuracy.