# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 5 Experimental Results
### Exploring an Optimal Architecture--CarNet, by Making Experiments on PKLot Dataset
#### 5.2.2 Experimental Results on CNRPark + EXT Dataset  
We trained Carnet, AlexNet and ResNet50 to evaluate how good is our model. Since these are the models which demonstrate highest testing scores on PKLot dataset we assume that making experiments only with these models would be enough. Training and validation accuracies of these architectures are given in Figure 13.  
The exact scores related to Figure 13 are provided by Table 11. Similar like training on PKLot dataset, the table also show that AlexNet illustrates the highest result in training and validation phases. Training and validation accuracies for CarNet are 97.91% and 90.05% respectively, showing the lowest scores. The highest score is depicted by AlexNet, 96.99% and 97.91%, for training and validation respectively. The last model which showed fairly high testing accuracies on PKLot dataset, ResNet50 showed training accuracy 96.51% while showing 97.80% validation accuracy. However these are only training and validation accuracies which are not most important number to evaluate a certain model. Ultimate assessment, testing accuracy scores are given in Table 12. CarNet has the highest scores on testing phase. \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}{*}{AlexNet [6]} & PUCPR & 98.60\% & 88.80\% & 83.40\% \\  & UFPR04 & 89.50\% & **98.20\%** & 87.60\% \\  & UFPR05 & 88.20\% & 87.30\% & **98\%** \\ \hline \multirow{3}{*}{CarNet} & PUCPR & **98.80\%** & **94.40\%** & **97.70\%** \\  & UFPR04 & **98.30\%** & 95.60\% & **97.60\%** \\  & UFPR05 & **98.40\%** & **95.20\%** & 97.50\% \\ \hline \multirow{3}{*}{VGG16 [52]} & PUCPR & 88.20\% & 94.20\% & 90.80\% \\  & UFPR04 & 89.70\% & 95.30\% & 90\% \\  & UFPR05 & 90.50\% & 94.90\% & 91.80\% \\ \hline \multirow{3}{*}{VGG16 (fine-tuned) [52]} & PUCPR & 69.10\% & 91.70\% & 92.60\% \\  & UFPR04 & 67.90\% & 95\% & 86.80\% \\  & UFPR05 & 91.20\% & 92.80\% & 92.80\% \\ \hline \multirow{3}{*}{VGG19 [52]} & PUCPR & 81.50\% & 93.80\% & 94.60\% \\  & UFPR04 & 80.40\% & 92.30\% & 91.90\% \\  & UFPR05 & 88.80\% & 95.10\% & 95.90\% \\ \hline \multirow{3}{*}{VGG19 (fine-tuned) [52]} & PUCPR & 84.70\% & 94.10\% & 91.90\% \\  & UFPR04 & 85.40\% & 94.10\% & 92.30\% \\  & UFPR05 & 87.60\% & 94.30\% & 95.40\% \\ \hline \multirow{3}{*}{Xception [53]} & PUCPR & 96.30\% & 92.50\% & 93.3 \\  & UFPR04 & 94\% & 94.60\% & 93.40\% \\  & UFPR05 & 95.70\% & 90.90\% & 91.20\% \\ \hline \multirow{3}{*}{Inception V3 [54]} & PUCPR & 90.80\% & 91.10\% & 94.2 \\  & UFPR04 & 91.70\% & 95.20\% & 92.40\% \\  & UFPR05 & 94.30\% & 92.9 & 93.70\% \\ \hline \multirow{3}{*}{ResNet50 [55]} & PUCPR & 90.50\% & 93.90\% & 94.10\% \\  & UFPR04 & 93.70\% & 94.80\% & 93.30\% \\ \cline{1-1}  & UFPR05 & 92.20\% & 94.80\% & 95.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results related to PKLot dataset [4]. Comparison of testing accuracies for Carmet with respect to other models. Figure 13: Experimenting on CNRPark + EXT dataset [5] to build an optimal model. x-direction: number of epochs (500), y-direction: accuracy. (**a**) training accuracy; (**b**) validation accuracy. Results are as we expected. Alexnet demonstrated second highest testing accuracy, 96.54%, while ResNet50 has the lowest testing score among other two architectures, with score 96.24%. The most noticeable consideration from experiments on the given datasets, deep models represent poorer results than models with less layers. Comparison of CarNet with Previous Approaches by Making Experiments on PKLot and CNRPark + EXT Datasets  
We compared testing scores of CarNet with prior works mentioned in [4; 5; 47]. However, ref. [5] uses both datasets, PKLot and CNRPark + EXT, to experiment their proposed architecture, while [4; 47] made experiments only on PKLot dataset. Moreover, ref.