# Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection
## 3 Results
### Feature Map Comparison of Different Networks  
Experiments were performed to compare different feature maps, which were obtained from parking slot images based on networks A, B, C, and the baseline. An optimal dataset that simultaneously maximizes the classification performance of all the designed networks was needed for the feature map comparison in the same condition. All the datasets used for the feature map analysis of the baseline were deployed to identify the optimal dataset. Table 2 shows the number of misclassifications in networks A, B, and C in various dataset cases. Figure 8: Visualized feature maps of the baseline. (**a**) Parking and (**b**) empty states. The red circles show the regions considered to contain important information. Figure 9: Examples of misclassifications and their visualized baseline feature maps. Misclassifications of (**a**) parking state and (**b**) empty state, (**c**) a visualized feature map obtained from (**a**), and (**d**) a visualized feature map obtained from (**b**). The four networks were designed to have the same input and output sizes for direct comparisons of their feature maps. Table 2 lists the number of misclassifications of the networks. The experimental results indicate that there are several optimal dataset candidates available for the feature map comparison. Among the candidates, ROI levels 0 to 1 were chosen as an optimal dataset to compare the features extracted by the different networks. Figure 10 shows the parking slot images and their feature maps extracted by network C.  
Figure 10g,h shows that the feature maps of the empty states contained many dark elements within the red circle. Figure 10e,f shows that the feature maps of the parking states contained many bright elements within the red circle. Therefore, Figure 10 indicates that network C extracted features from the images that were similar to those extracted using the baseline network. Figure 11 shows a direct comparison of the feature maps extracted by the baseline network and network C. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**Number of Misclassifications**} \\ \cline{2-5}  & **Baseline** & \begin{tabular}{c} **Network A** \\ **(FP1 + CP)** \\ \end{tabular} & \
\begin{tabular}{c} **Network B** \\ **(FP2 + CP)** \\ \end{tabular} &
\begin{tabular}{c} **Network C** \\ **(FP3 + CP)** \\ \end{tabular} \\ \hline ROI levels 0 to 4 & 1 & 1 & 3 & 1 \\ ROI levels 0 to 3 & 0 & 1 & 1 & 2 \\ ROI levels 1 to 4 & 1 & 2 & 1 & 0 \\ ROI levels 0 to 2 & 0 & 0 & 0 & 0 \\ ROI levels 1 to 3 & 0 & 0 & 0 & 0 \\ ROI levels 2 to 4 & 1 & 0 & 1 & 0 \\ ROI levels 0 to 1 & 0 & 0 & 0 & 0 \\ ROI levels 1 to 2 & 1 & 3 & 1 & 2 \\ ROI levels 2 to 3 & 0 & 0 & 0 & 0 \\ ROI levels 3 to 4 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of misclassifications based on various networks and datasets. Figure 10: Examples of input images and their feature maps extracted by network C. Parking state images of (**a**) ROI level 0 and (**b**) ROI level 1. Empty state images of (**c**) ROI level 0 and (**d**) ROI level 1. (**e-h**) Feature maps individually corresponding to (**a–d**). Similarities were found in the feature maps extracted by the baseline and network C. While many bright elements were contained within the red circle in the feature maps obtained from the parking state images, many dark elements were contained within the red circle in the feature maps obtained from the empty state images, independent of the network that was employed for feature extraction. Consequently, the different networks extracted similar features from the parking slot images to classify the parking and empty states. In addition, the inference time of network C was estimated in the same way as the baseline. The inference time of network C without preprocessing was approximately 7.26 ms on average, and the inference time with preprocessing was nearly 8.09 ms on average. Thus, network C operated at approximately 6.18 FPS when only 20 parking slots were considered in a frame.