# Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection
## 3 Results
### Feature Map Analysis of the Baseline Network  
Experiments were conducted to analyze the characteristic of the features extracted from the parking slot images by the baseline network. The dataset that maximized the performance of the baseline was selected to enhance the reliability of the analyzed results. Table 1 lists the training and testing results of the baseline network.  
ROI levels 0 to 4 indicate that the dataset used for the experiments contained the entire ROI levels. ROI levels 0 to 1 indicate that the dataset contained ROI levels 0 and 1 only. The datasets were divided into training, validation, and testing sets based on a fixed ratio; this ratio was 69% for training, 17% for validation, and 14% for testing. The baseline network used training sets for the update of weights and validation and test sets for inspection only. The validation sets helped inspect the classification performance of the baseline network during training. The test sets confirmed the generalization performance of the baseline, as the baseline had never seen the test sets during training. Misclassification indicates the number of erroneous classifications among the test sets when these were input to the baseline network. In general, the baseline had one or zero misclassifications. Consequently, the following feature analysis is reliable because the classification performance is convincing.  
Images of the parking and empty states in the test set of the dataset were visualized to interpret the feature extraction mechanism of the baseline network. The qualitative information of the features extracted by the baseline network was provided by the proposed visualization method. The features extracted from the images of the parking and empty states were compared based on their qualitative information. Figure 8 shows that there was a difference between the images of the parking and empty states.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{4}{c}{**Number of Datapoints**} \\ \cline{2-5}  & **Training** & **Validation** & **Test** & **Misclassification** \\ \hline ROI levels 0 to 4 & 1850 & 460 & 390 & 1 \\ ROI levels 0 to 3 & 1480 & 368 & 312 & 0 \\ ROI levels 1 to 4 & 1480 & 368 & 312 & 1 \\ ROI levels 0 to 2 & 1110 & 276 & 234 & 0 \\ ROI levels 1 to 3 & 1110 & 276 & 234 & 0 \\ ROI levels 2 to 4 & 1110 & 276 & 234 & 1 \\ ROI levels 0 to 1 & 740 &A visualized feature map has many bright elements within the red circle in the parking state. However, a visualized feature map also has fewer bright elements within the red circle in the empty state. Not all feature maps yield the same results, but the visualized feature maps of the parking states tend to have more bright elements within the red circle than those of the empty states. Figure 9 supports our observations.  
In Figure 9, the green numbers in brackets indicate the classification results. Zero means that the parking slot was empty, and one means the parking slot was full. The green decimal numbers represent the class probabilities. The green numbers in Figure 9 indicate the empty state with a 74% probability. However, this is a misclassification, because it is the parking state. Figure 9 shows the reason for this misclassification. A visualized feature map of the parking case should have many bright elements within the red circle; instead, many dark or ambiguous elements are shown in Figure 9. Figure 9 is the empty state, but many relatively bright elements are within the red circle in Figure 9. It is verified that specific regions of feature maps are activated if the parking slot is full in correct classification. Additionally, experiments were conducted to survey the response time of our classifiers. The inference time of the baseline without preprocessing was nearly 8.00 ms on average with respect to one parking slot image. The inference time with preprocessing was approximately 9.29 ms on average. Thus, the baseline operates at approximately 5.3 FPS when only 20 parking slots are considered in a frame.