Vehicle detection is essential for urban planning, traffic management, and various security and surveillance applications. This study compares the performance of two popular deep learning algorithms, Faster R-CNN and SSD, for vehicle detection using traffic video recordings obtained from unmanned aerial vehicles. The dataset, comprising images taken from diverse angles, heights, and lighting conditions, was utilized to assess the effectiveness of both models. Faster R-CNN demonstrated superior accuracy in detecting vehicles and motorcycles under various conditions, including night scenarios and scenes with obstacles such as shadows, traffic signs, and light poles. However, it struggled with detecting very small objects and those with color tones similar to the background. In contrast, while SSD performed well under optimal lighting, it exhibited limitations in detecting smaller vehicles partially obscured by environmental elements. Quantitative analysis revealed that Faster R-CNN had 13.21% higher precision, 12.5% higher recall, and 10.04% higher mAP compared to SSD. Despite its superior detection performance, Faster R-CNN's longer detection time indicates that SSD may be more suitable for real-time applications where speed is crucial.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Performance Comparison of Faster R-CNN and SSD in Vehicle Detection from Aerial Traffic Videos


    Beteiligte:


    Erscheinungsdatum :

    16.10.2024


    Format / Umfang :

    779398 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos

    Lu, Duo / Eaton, Eric / Weg, Matt et al. | ArXiv | 2023

    Freier Zugriff

    Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos

    Ke, Ruimin / Li, Zhibin / Kim, Sung et al. | IEEE | 2017



    Vehicle parameterization and tracking from traffic videos

    Anh Vu, / Boriboonsomsin, K / Barth, M | IEEE | 2010


    VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VIDEOS

    M. Shahbazi / S. Simeonova / D. Lichti et al. | DOAJ | 2020

    Freier Zugriff