Aerial imaging and object detection with unmanned aerial vehicle (UAV) systems present unique challenges, including varying altitudes, dynamic backgrounds, and changes in lighting and weather conditions. These factors complicate the detection process, demanding robust and adaptive algorithms. Furthermore, the need for real-time processing in UAV applications imposes stringent requirements on computational efficiency and resource management. This study presents a comparative analysis of two cutting-edge object detection algorithms, YOLOv9 and YOLOv10, specifically tailored for vehicle detection in UAVcaptured traffic images. Leveraging a custom dataset derived from UAV aerial imaging, both algorithms were trained and evaluated to assess their performance in terms of speed and accuracy. The experimental results reveal that while YOLOv9 demonstrates a marginally superior inference speed, YOLOv10 excels slightly in detection accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Challenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10


    Contributors:


    Publication date :

    2024-10-22


    Size :

    8098008 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Car Object Detection: Comparative Analysis of YOLOv9 and YOLOv10 Models

    Ardac, Fatma Betul Kara / Erdogmus, Pakize | IEEE | 2024


    BRA-YOLOv10: UAV Small Target Detection Based on YOLOv10

    Quanyu Zhang / Xin Wang / Heng Shi et al. | DOAJ | 2025

    Free access


    Real-Time Vehicle Detection and Air Pollution Estimation Using YOLOv9

    Hari Suparwito / Bernardus Hersa Galih Prakoso / Rosalia Arum Kumalasanti et al. | DOAJ | 2025

    Free access