# Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection
## 4 Discussion  
The use of NNs for vacant parking slot detection through image classification has been widespread. NNs have overcome the need to develop feature extraction methods based on manual image analyses or to change feature extraction methods whenever the detecting environment changes [21, 23]. However, most studies on vacant parking slot detection employing NNs do not determine which features are extracted from images to accomplish accurate classifications [24, 35]. The current study determined which features were extracted by NNs and investigated these by using the proposed feature map visualization method. If NNs have a low-classification performance, then the feature analysis result will be unreliable. However, previous studies have verified that diversified data improve the classification performance of NNs [34, 35]. For this reason, the datasets for the experiments were diversified to maximize the classification performance of NNs. Among the datasets, the optimal dataset (which maximized the performance) was utilized for the feature map analysis. There were different patterns of feature maps extracted by the NNs according to the status of the parking slots. Relatively bright elements existed within a specific region in the feature maps of the parking state, and dark elements existed in corresponding regions in the feature maps of the empty state. It was assumed that the reason why the pattern difference occurred was due to vehicle bonnets. The region that showed pattern differences was consistent with the position of vehicle bonnets if cars were in slots. However, the region corresponded to the ground surface if there was no car. Therefore, it was concluded that the NNs used the information of the region as a crucial factor for classifying the parking slot states. As a result, our findings demonstrate that the characteristics of the features extracted by NNs can be intuitively recognized. Many studies have been conducted to understand how NNs find solutions. One study suggested a method to indicate which regions in images are considered crucial factors for finding and classifying objects; these regions were found to be consistent with the positions of the objects [26]. Another study made important features more useful to performance than trivial features, although critical features were not shown in a human-recognizable way [28]. However, these studies cannot be easily compared with our study because the viewpoints are slightly different. Thus, expanded experiments were conducted to support  
Figure 11: Examples of feature maps extracted by the baseline network and network C. Feature map extracted by the baseline network in the (**a**) parking and (**c**) empty states; feature maps extracted by network C in the (**b**) parking and (**d**) empty states. our analysis by investigating the various features extracted by different NNs. As performed, an optimal dataset that simultaneously maximized the performance of different NNs was employed for the feature map analysis. Similar characteristics were found in the features extracted from the images, regardless of the used NNs, and the result of the feature map analysis was consistent with the results of our experiment on the baseline network. Thus, the results indicate that NNs classify parking slot images by extracting features based on a specific rule. Our study can be deployed in various ways. First, a feature map analysis can help in finding the basis of network compression. Most network compression studies emphasize crucial weights or exclude trivial weights. Those studies conclude that network compression is successful if compressed NNs and uncompressed NNs have similar performances. However, a similar performance does not guarantee that one is a compressed version of the other. Therefore, reasonable explanations are needed to ensure the correlation of different networks. In that context, a feature map analysis can be used to determine the correlation.