Vehicle detection in UAV images is constrained by the differences in environmental and technical conditions such as target type and size, degree of occlusion, acquisition altitude, illumination, and weather. High levels of false alarms and missed detections are the most prominent challenges faced by traditional detection algorithms, particularly in the case of occluded or tiny targets acquired by UAV. This research sustains such problems by employing data augmentation and transfer learning techniques with an adjustable YOLOv9 model on UAV image dataset. We apply focused data augmentation techniques to enhance model performance over varied use cases, and transfer learning turns out to be effective in adapting the model to work for UAV-based occluded vehicle detection. The targeted improvements in the modified architecture of YOLOv9 enables effective detection of vehicles, including occluded ones with different sizes and orientations, thus, minimizing false alarms. The effectiveness of the proposed approach was experimentally validated, outperforming the existing ones and providing an architecturally and practically adequate solution for vehicle detection on real UAVs.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Detection in UAV Images Using Data Augmentation and Transfer Learning with Modified YOLOv9


    Contributors:


    Publication date :

    2024-12-02


    Size :

    511661 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    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

    Automatic License Plate Detection Using YOLOv9

    V, Nivethitha / Rajan, Shruthika / Sriram, Suthir et al. | IEEE | 2024


    Enhanced YOLOv9 for Pedestrian and Vehicle Detection in Foggy Traffic Scenarios

    Xiao, Xianghui / Zeng, Junbin / Guan, Luchang et al. | IEEE | 2024


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

    Bakirci, Murat / Dmytrovych, Petro / Bayraktar, Irem et al. | IEEE | 2024


    Enhanced Civilian Airport Detection with Optical Imaging and YOLOv9

    Panneerselvam, Ramesh Kumar / Arekapudi, Bhuvan Deep / Shaik, Nagur Vali | IEEE | 2024