Normalization Layer Enhancement in Convolutional Neural Network for Parking Space Classification

Abstract

The research problem of this study is the urgent need for real-time parking availability information to assist drivers in quickly and accurately locating available parking spaces, aiming to improve upon the accuracy not achieved by previous studies. The objective of this research is to enhance the classification accuracy of parking spaces using a Convolutional Neural Network (CNN) model, specifically by integrating an effective normalizing function into the CNN architecture. The research method employed involves the application of four distinct normalizing functions to the EfficientParkingNet, a tailored CNN architecture designed for the precise classification of parking spaces. The results indicate that the EfficientParkingNet model, when equipped with the Group Normalization function, outperforms other models using Batch Normalization, Inter-Channel Local Response Normalization, and Intra-Channel Local Response Normalization in terms of classification accuracy. Furthermore, it surpasses other similar CNN models such as mAlexnet, you only look once (Yolo)+mobilenet, and CarNet in the same classification task. This demonstrates that EfficientParkingNet with Group Normalization significantly enhances parking space classification, thus providing drivers with more reliable and accurate parking availability information.