# Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection
###### Abstract  
Vacant parking slot detection using image classification has been studied for a long time. Currently, deep neural networks are widely used in this research field, and experts have concentrated on improving their performance. As a result, most experts are not concerned about the features extracted from the images. Thus, no one knows the crucial features of how neural networks determine whether a particular parking slot is full. This study divides the structures of neural networks into feature extraction and classification parts to address these issues. The output of the feature extraction parts is visualized through normalization and grayscale imaging. The visualized feature maps are analyzed to match the feature characteristics and classification results. The results show that a specific region of feature maps is activated if the parking slot is full. In addition, it is verified that different networks whose classification parts are identical extract similar features from parking slot images. This study demonstrates that feature map analyses help us find hidden characteristics of features and understand how neural networks operate. Our findings show a possibility that handcrafted algorithms using the features found by machine learning algorithms can replace neural network-based classification parts. vacant parking slot detection; image classification; deep neural networks; feature extraction; feature map visualization +
Footnote †: journal: _sciences_  
HOGs divide an image into many cells and generate one histogram for each cell; in turn, the histograms consist of the magnitudes and directions of all the pixel gradients in the cells. Several authors have used HOGs to train conventional machine learning algorithms, such as SVM and k-nearest neighbor [15] or Bayesian hierarchical frameworks, which are statistical models [16]. Additionally, there are many feature extraction methods, and these methods require the use of processed pixel colors for template matching [13; 17], the application of a morphological operation to binary images [18], or the extraction of texture information through local binary patterns and local phase quantization [19]. These feature extraction methods help improve the accuracy of vacant parking slot detection, but generalization is difficult because each method is differently affected by the environment. For this reason, conventional feature extraction methods are limited, as they cannot be easily applied to different situations such as camera angle and direction variations, illumination, and weather conditions. Experts have started to deploy neural networks (NNs) to overcome these limitations while maintaining the accuracy of vacant parking slot detection methods. Convolutional NNs (CNNs) have become the structural base of many NNs and have demonstrated remarkable image-processing performances. To identify vacant parking slots, researchers have employed CNN-based NNs as classifiers [20; 21], which determine the status of each parking slot by using the slot image. However, the use of NNs as classifiers is disadvantageous, in that the initialization of the regions-of-interest (ROIs) is necessary because only one parking slot image is input into the classifiers at once. For this reason, classifiers must repeat their operations until all the classifications of each parking slot are completed. Researchers have deployed NNs as detectors (which can simultaneously identify several vacant parking slots) to surmount the disadvantages of classifiers. A combination of YOLOv3 and ResNet used a public dataset for training and testing and performs well as a detector [22]. A faster R-CNN has verified its excellent performance as a detector [23]. A variation of the ConvNeXT model attains an accuracy of >99% on public datasets [24]. Likewise, many studies have demonstrated that feature extraction using NNs is better for identifying vacant parking slots than feature extraction using conventional methods [20; 21; 25].