# 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
[5] provided their final results as a testing accuracy scores while [4; 47] provided as AUC (Area Under the Curve) scores. Thus, we calculated AUC scores for CarNet experiments in order to be able to compare its performance with [4; 47]. Initially, we show testing scores for CarNet and mAlexNet [5] only on PKLot dataset. Comparison results with mAlexNet [5] on PKLot dataset are given by Table 13. As one may notice, there are nine combinations of training/testing subsets. In four cases out of nine mAlexNet showed better performance than Carnet, while in another five cases CarNet outperforms mAlexNet. In the order of training/testing subsets, the cases mAlexNet has higher results are UFPR04/UFPR04, UFPR05/UFPR05, PUCPR/UFPR04 and PUCPR/PUCPR with values 99.54%, 99.49%, 98.03% and 99.9% respectively. The case CarNet achieves higher testing scores are UFPR04/UFPR05, UFPR04/PUCPR05/UFPR04, UFPR05/PUCPR and PUCPR/UFPR05 with values 97.6%, 98.3%, 95.2%, 98.4% and 97.6% respectively. An important thing to notice here is CarNet showed better generalization while mAlexNet mostly having better testing results when they train and test on same subset of images. Final column of the table shows the average testing accuracies for both architectures. Mean accuracies for mAlexNet and CarNet are 96.74% and 97.04% respectively, showing slightly higher accuracy score by CarNet. Following, we compared CarNet with mAlexNet on CNRPark + EXT and PKLot datasets. Testing scores for both approaches are given by Table 14. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Method** & **Training Accuracy** & **Validation Accuracy** \\ \hline CarNet & 97.91\% & 90.05\% \\ AlexNet [6] & **96.99\%** & **97.91\%** \\ ResNet50 [55] & 96.51\% & 97.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparisons of training and validation accuracies for CarNet, AlexNet [6] and ResNet50 [55] on CNRPark + EXT dataset [5]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Method** & **Testing Accuracy** \\ \hline CarNet & **97.24\%** \\ AlexNet [6] & 96.54\% \\ ResNet50 [55] & 96.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparisons of testing scores for CarNet, AlexNet [6] and ResNet50 [55] on CNRPark + EXT dataset [5]. Results are as we expected. Furthermore, We tried to test both architecture in combination of both datasets. However, mAlexNet shows very poor results when training on PKLot and testing on CNRPark or in a reverse case. Given three combination of testing scores. In all cases, CarNet shows much more robustness. As we claimed before, the main advantage of CarNet is that it is well-generalized. It shows good performance when training on a certain dataset and testing on a different dataset. The last type of indications we will be showing are comparisons of AUC scores for CarNet with respect [4; 47] on PKLot dataset. Ref. [47] is the another recent proposal achieving one of the highest performances so far, using image processing and machine learning. The authors of [47] extracted ten features from an image: three channels of LUV color space, gradient magnitude as another feature and six quantized gradient channels. Following, they fed these features into Logistic Regression (LR) and Support Vector Machine (SVM). We will be referring the approach provided by [4] as PKLot and the approach provided by [47] as [PERSON] et al. (LR) and [PERSON] et al. (SVM). Comparisons of four types of scores are provided by Table 15. Among the results, [PERSON] et al. (LR) shows best performance only in one case, training/testing for UFPR04/UFPR05. PKLot, ref. [4], demonstrates best results on three cases, training/testing for UFPR04/UFPR04, UFPR05/UFPR05 and PUCPR/UFPR05. From these results, we may conclude that PKLot is very good while training and testing on same dataset. The AUC scores for CarNet outperforms other two methods in five cases out of nine. The cases Carnet shows highest scores are UFPR04/UFPR05, UFPR04/PUCPR, UFPR05/PUCPR, PUCPR/UFPR04, PUCPR/UFPR05 with values 99.35, 99.82, 97.91, 98.45 and 99.38 respectively.