Excessive booming global population creates a widespread increase in traffic on Hi-tech modern roads. It is very much essential to detect vehicles and computing traffic congestion on highways. The key aspect is to hoard vehicle data through number plate detection. A deep learning model or Network has been developed to detect vehicles passes dynamically and efficiently. In the proposed work, a deep learning-based algorithm is proposed for detecting both vehicles and number plates for a reputed company surveillance dataset. The proposed model uses a video dataset as an input and the video has been segmented into several frames. Using pre-trained weights and labels of the dataset, the vehicles and its number plate are detected by the dark flow toolkit. This tool provides to extract the region of the vehicle with proper annotation. In future work, the proposed model aims to calculate the speed of the vehicle based on the surrounding area.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Tracing of Vehicle Region and Number Plate Detection using Deep Learning


    Contributors:


    Publication date :

    2020-02-01


    Size :

    110956 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle Number Plate Detection using Deep Learning

    Krishna, K.V.S.S. Rama / Rajyalakshmi, P. / Asritha, K. Lakshmi et al. | IEEE | 2024


    Car Number Plate Detection using Deep Learning

    Kushwaha, Mohit Kumar / Suseela, G | IEEE | 2022


    Automatic vehicle license plate number detection using machine learning

    Rondla, Saikiran Reddy | BASE | 2020

    Free access

    Vehicle license plate detection using morphological operations and deep learning

    Hezil, Nabil / Amrouche, Aissa / Bentrcia, Youssouf | IEEE | 2022


    Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model

    Gupta, Shashi Kant / Saxena, Surabhi / Khang, Alex et al. | IEEE | 2023