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.