Object detection is a critical task that becomes difficult when dealing with onboard detection using aerial images and computer vision technique. The main challenges with aerial images are small target sizes, low resolution, occlusion, attitude, and scale variations, which affect the performance of many object detectors. The accuracy of the detection and the efficiency of the inference are always trade-offs. We modified the architecture of CenterNet and used different CNN-based backbones of ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Res2Net50, Res2Net101, DLA-34, and hourglass14. A comparison of the modified CenterNet with nine CNN-based backbones is conducted and validated using three challenging datasets, i.e., VisDrone, Stanford Drone dataset (SSD), and AU-AIR. We also implemented well-known off-the-shelf object detectors, i.e., YoloV1 to YoloV7, SSD-MobileNet-V2, and Faster RCNN. The proposed approach and state-of-the-art object detectors are optimized and then implemented on cross-edge platforms, i.e., NVIDIA Jetson Xavier, NVIDIA Jetson Nano, and Neuro Compute Stick 2 (NCS2). A detailed comparison of performance between edge platforms is provided. Our modified CenterNet combination with hourglass as a backbone achieved 91.62%, 75.61%, and 34.82% mAP using the validation sets of AU-AIR, SSD, and VisDrone datasets, respectively. An FPS of 40.02 was achieved using the ResNet18 backbone. We also compared our approach with the latest cutting-edge research and found promising results for both discrete GPU and edge platforms.


    Access

    Download


    Export, share and cite



    Title :

    On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)


    Contributors:


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Real-time visual object tracking for unmanned aerial vehicles (UAVs)

    GAO YU / WANG XING / MA RUI et al. | European Patent Office | 2023

    Free access

    Unmanned Aerial Vehicles (UAVs) in Firefighting

    Orgeira-Crespo, Pedro / Rey, Guillermo / Ulloa, Carlos et al. | Springer Verlag | 2024


    Drone/Unmanned Aerial Vehicles (UAVs) Technology

    Adepoju, Omoseni | Springer Verlag | 2021


    Launcher for unmanned aerial vehicles (UAVs)

    GRONDIN TIMOTHY / SCHICHO ANDREW / DELAY PATRICK et al. | European Patent Office | 2025

    Free access