The Intelligent Transportation System (ITS) enhances transportation safety by addressing traffic challenges, with vehicle detection and classification as key components. Unmanned aerial vehicles (UAVs) have gained prominence in computer vision applications; however, vehicle classification using UAV imagery remains challenging due to factors such as small object sizes, varied orientations, and environmental impacts. This study introduces a robust method tailored to the unique conditions of the Iraqi Kurdistan Region, leveraging a newly developed dataset of 2,919 images categorized into five vehicle classes (motorcycle, personal car, taxi, truck, and bus). A modified YOLOv4 algorithm, enhanced with the Convolutional Block Attention Module (CBAM), was proposed to improve detection accuracy and focus on critical features. Experimental results show that the proposed system achieves a mean average precision (mAP) of 88.25% and an inference speed of 35 frames per second, outperforming Faster Region-Convolutional Neural Network (Faster RCNN), YOLOv3, and baseline YOLOv4 when both speed and accuracy are considered.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    YOLO-Based Approach for Multiple Vehicle Detection and Classification using UAVs in the Kurdistan Region of Iraq


    Weitere Titelangaben:

    Int. J. ITS Res.


    Beteiligte:


    Erscheinungsdatum :

    01.08.2025


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Human Detection Based Yolo Backbones-Transformer in UAVs

    Do, Manh-Tuan / Ha, Manh-Hung / Nguyen, Duc-Chinh et al. | IEEE | 2023


    The challenges impeding traffic safety improvements in the Kurdistan Region of Iraq

    Hemin Mohammed, Ph.D. / Dilshad Jaff, MPH / Steven Schrock, Ph.D. | DOAJ | 2019

    Freier Zugriff

    NUAV-YOLO: a lightweight object detection algorithm based on YOLOv8 for UAVs

    Xia, Jinlong / Teng, Fei / Feng, Li et al. | SPIE | 2025


    Parallel region coverage using multiple UAVs

    Agarwal, A. / Lim Meng Hiot, / Nguyen Trung Nghia, et al. | IEEE | 2006