# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 6 Conclusions  
An efficient solution--CarNet, for visual detection of a parking status, was presented which uses Dilated Convolutional Neural Networks. CarNet is provided as a robust design for employees to classify images of parking spaces taken from a camera as occupied or vacant. In this proposed approach, we used three contributions: dilated convolutional, a small number of layers, large window sizes. We showed by experiments that the current task can be tackled effectively by using these contributions. To assess the performance of CarNet and to be able to compare it with respect to other architectures, we made our experiments on PKLot dataset since prior attends were evaluated on this dataset as well. PKLot contains images with high variability related to occlusions, various point of views, illumination and weather conditions such as sunny, rainy, and overcast days. This \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of Architecture** & **Training Set** & **Testing Test** & **AUC Score** & **Method Achieved Best Result** \\ \hline
**CarNet** & UFPR04 & UFPR04 & 97.9 & \\
**[PERSON] et al. (LR)**[47] & UFPR04 & UFPR04 & 0.9994 & PKLot [4] \\
**[PERSON] et al. (SVM)**[47] & UFPR04 & UFPR04 & 0.9996 & \\
**PKLot**[4] & UFPR04 & UFPR04 & **0.9999** & \\ \hline
**CarNet** & UFPR04 & UFPR05 & **99.35** & \\
**[PERSON] et al. (LR)**[47] & UFPR04 & UFPR05 & 0.9928 & CarNet \\
**[PERSON] et al. (SVM)**[47] & UFPR04 & UFPR05 & 0.9772 & \\
**PKLot**[4] & UFPR04 & UFPR05 & 0.9595 & \\ \hline
**CarNet** & UFPR04 & PUCPR & **99.82** & \\
**[PERSON] et al. (LR)**[47] & UFPR04 & PUCPR & 0.9881 & CarNet \\
**[PERSON] et al. (SVM)**[47] & UFPR04 & PUCPR & 0.9569 & \\
**PKLot**[4] & UFPR04 & PUCPR & 0.9713 & \\ \hline
**CarNet** & UFPR05 & UFPR04 & 97.96 & \\
**[PERSON] et al. (LR)**[47] & UFPR05 & UFPR04 & **0.9963** & [PERSON] et al. (LR) [47] \\
**[PERSON] et al. (SVM)**[47] & UFPR05 & UFPR04 & 0.9943 & \\ PKLot [4] & UFPR05 & UFPR04 & 0.9533 & \\ \hline
**CarNet** & UFPR05 & UFPR05 & 99.89 & \\
**[PERSON] et al. (LR)**[47] & UFPR05 & UFPR05 & 0.9987 & PKLot [4] \\
**[PERSON] et al. (SVM)**[47] & UFPR05 & UFPR05 & 0.9988 & \\
**PKLot**[4] & UFPR05 & UFPR05 & **0.9995** & \\ \hline
**CarNet** & UFPR05 & PUCPR & **97.91** & \\
**[PERSON] et al. (LR)**[47] & UFPR05 & PUCPR & 0.9779 & \\
**[PERSON] et al. (SVM)**[47] & UFPR05 & PUCPR & 0.9405 & CarNet \\
**PKLot**[4] & UFPR05 & PUCPR & 0.9761 & \\ \hline
**CarNet** & PUCPR & UFPR04 & **98.45** & \\
**[PERSON] et al. (LR)**[47] & PUCPR & UFPR04 & 0.9829 & CarNet \\
**[PERSON] et al. (SVM)**[47] & PUCPR & UFPR04 & 0.9843 & CarNet \\
**PKLot**[4] & PUCPR & UFPR04 & 0.9589 & \\ \hline
**CarNet** & PUCPR & UFPR05 & **99.38** & \\
**[PERSON] et al. (LR)**[47] & PUCPR & UFPR05 & 0.9457 & CarNet \\
**[PERSON] et al. (SVM)**[47] & PUCPR & UFPR05 & 0.9401 & CarNet \\
**PKLot**[4] & PUCPR & UFPR05 & 0.9152 & \\ \hline
**CarNet** & PUCPR & UFPR05 & 99.86 & \\
**[PERSON] et al. (LR)**[47] & PUCPR & UFPR05 & 0.9994 & PKLot [4] \\
**[PERSON] et al. (SVM)**[47] & PUCPR & UFPR05 & 0.9999 & \\
**PKLot**[4] & PUCPR & UFPR05 & **0.9999** & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of AUC scores of CarNet with [PERSON] et al. (LR) [47] and [PERSON] et al. (SVM) [47] on PKLot dataset. makes the dataset more compatible with a real scenario of an outdoor parking lot, and represents a good complement to other publicly available datasets, for more reliable assessments. We performed experiments to compare the performance and generalization capabilities of our approach against other state-of-the-art techniques. These experiments confirm that our proposed method outperforms AlexNet and other well-known architectures. CarNet represents higher accuracy for totally new images compared to prior approaches. The proposed approach helps drivers to find available parking spaces quickly, and thus decreases traffic load, air pollution, and possibly illegal parking.