Traffic analysis is one of the crucial tasks of intelligent transport system that utilizes deep learning for range of purposes. Many tasks, such as vehicle recognition, vehicle counting, traffic violation monitoring, vehicle speed monitoring, vehicle density and so on, can be accomplished by using cameras installed in strategic locations along roads. In this paper powerful deep learning techniques such as (Yolov5, Mask R-CNN, SSD) and state-of-the-art object tracking algorithm known as DeepSORT was used to perform real time vehicle detection and counting in a video. A new highway vehicle detection dataset with overall of 32,265, instances of four vehicle classes named: bus, car, motorbike, truck was created in this paper and utilized for training vehicle detection and counting system. Result shows that average counting accuracy by using Yolov5 combined DeepSORT reaches to 95% while reaches to 91% by using Mask R-CNN combined DeepSORT and 84% by using SSD combined DeepSORT in hard environment. From the experimental work, counting accuracy by using Yolov5 outperforms other two deep learning techniques.


    Access

    Download


    Export, share and cite



    Title :

    Vehicle Detection, Counting, and Classification System based on Video using Deep learning Models


    Contributors:


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Vehicle Counting System Based on Vehicle Type Classification Using Deep Learning Method

    Awang, Suryanti / Azmi, Nik Mohamad Aizuddin Nik | Springer Verlag | 2017


    Vehicle Classification and Counting for Traffic Video Monitoring Using YOLO-v3

    Bose, Samprit / Ramesh, Chavan Deep / Kolekar, Maheshkumar H. | IEEE | 2022


    Vehicle Counting System using Deep Learning and Multi-Object Tracking Methods

    Liang, Haoxiang / Song, Huansheng / Li, Huaiyu et al. | Transportation Research Record | 2020