# Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection
###### Abstract
Vacant parking slot detection using NNs is advantageous, in that it can be applied to various situations without considering feature-extraction methods. In addition, there are many ways to improve the performance of NNs, and an adequate data quantity can enhance the fidelity and robustness of NNs. For these reasons, NNs are replacing conventional feature extraction methods. However, when using NNs, researchers do not need to manually extract useful features from images; therefore, NNs extract suitable features based on their own decisions. As a result, most researchers only concentrate on achieving good results rather than analyzing the features extracted by NNs. These result-oriented perspectives are eventually unable to solve fundamental problems because the processes are not understood. Various feature analysis methods have been studied to determine the internal processes of NNs. Class activation mapping (CAM) informs researchers of the region that NNs importantly address in images [26]. Gradient-weighted CAM is a generalized CAM that can be applied regardless of the types of NNs [27]. The convolutional block attention module refines features by unifying channel and spatial domain information [28]. Pyramid feature attention enhances feature diversity by applying spatial attention modules to low-level features and channel attention modules to high-level features [29]. Efficient channel attention improves the performance of NNs by overcoming dimensionality reduction issues that many channel attention-based methods encounter [30]. SimAM builds three-dimensional attention modules by calculating each neuron's importance and refines features using the module [31]. These studies effectively utilize the features extracted by NNs. However, they still cannot determine the characteristics of the features. Therefore, this study focuses on the determination of the characteristics of the features extracted from images by analyzing the features based on a feature map visualization. Figure 1 shows the differences between our proposal and other studies. In addition, the analysis and comparison of the features extracted by different NNs help us understand how NNs deal with vacant parking slot detection. The contributions of this study are as follows: (1) it is verified that the features extracted by NNs have specific patterns through a comparison and analysis based on feature map visualization. In addition, it is identified that it is possible to replace the classification part of NNs with handcrafted algorithms. (2) Various NNs were designed for systematic feature map analysis, and it is verified that NNs with different feature extraction structures extract similar features from images. (3) A new dataset is constructed to simplify vacant parking slot detection, and an ROI initialization method to optimize the performance of NNs is proposed. Section 2 explains the proposed methods and materials in detail. Section 3 shows the experimental results with diverse materials, such as figures and tables. Section 4 discusses the results thoroughly. Section 5 concludes this study.