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DESIGN OF DEEP LEARNING MODEL APPLIED FOR SMART PARKING SYSTEM

An Van Vo, Van Duc Phan, Vu Minh Bui, Tri Nhut Do

DOI: 10.15598/aeee.v21i4.5366


Abstract

In this article, a smart parking system using RFID technology incorporating a Deep Learning model for the purpose of identifying license plates is proposed and introduced. It tries to simulate the ability of the brain to recognize, differentiate and learn patterns from data. The employed algorithms are mainly based on neural network models where neurons are organized in layers that are stacked. The system is designed with the function of management in terms of incoming and outgoing vehicles by collecting and processing images as well as data on passenger information for updating parking status with the information of empty lot. Another function of the parking system also designed is a fully automatic method of paying the parking fee by the user. Deep learning model applied for smart parking system is implemented by utilizing Raspberry PI 3 embedded system and sensors. Experimental results with the plate identification rate in the worst condition up to 80% have proven the reliability of proposed smart parking system. In terms of quantity, the percentage of the worst plate identification down to 10% has proven the stability of proposed smart parking system.

Keywords


Deep learning, Smart Parking System, RFID, Raspberry.

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