This study aims to detect vehicles that are on the side of the parking lot so that it can be used as a smart parking system for parking management and find out information on the availability of parking spaces. In this study, the authors used the Haar Cascade Classifier, and YOLOv3 then compared them to get the best accuracy in detecting parked cars. The test was carried out using ten different scenarios, the highest accuracy obtained in this study was 96.88% using YOLOv3 with a probability of 90%. In contrast, the accuracy obtained by using the Haar Cascade Classifier is 63.34%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Car Detection in Roadside Parking for Smart Parking System Based on Image Processing




    Publication date :

    2020-07-01


    Size :

    624219 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Roadside parking detection system

    LI QIJIA / WANG TAO / QU FEIYU | European Patent Office | 2020

    Free access

    Unmanned roadside parking space parking management system

    LI DAPENG / ZHONG BINGDA / PAN RUN et al. | European Patent Office | 2024

    Free access

    Roadside parking space parking management method

    LI DAPENG / ZHONG BINGDA / WANG YANQI et al. | European Patent Office | 2024

    Free access

    Dynamic roadside parking system

    CHENG GUANGWEI | European Patent Office | 2020

    Free access

    Roadside parking management system

    KOU WANGDONG / JIANG HAIYANG / LI HONGJIAN | European Patent Office | 2021

    Free access