Real-time object recognition systems are critical for several UAV applications since they provide fundamental semantic information of the aerial scene. In this study, we describe how image quality limits object detection frame-works such as YOLO which can distinguish 80 different object classes. This paper will focus on vehicles such as cars, trucks and buses. Pristine high-resolution images are degraded using different blurring functions, spatial resolution, reduced image contrast, additive noise and lossy compression. Object recognition results are significantly better after applying an image super-resolution algorithm to realistically simulated under-sampled imagery.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Image quality and super resolution effects on object recognition using deep neural networks


    Beteiligte:

    Kongress:

    Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications ; 2019 ; Baltimore,Maryland,United States


    Erschienen in:

    Proc. SPIE ; 11006


    Erscheinungsdatum :

    2019-05-10





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Image Super-Resolution Using Quality Aware Generative Adversarial Networks

    Jinzhen, Mu / Shuo, Zhang / Yu, Zhang et al. | Springer Verlag | 2021


    Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition

    Cao, Y. / Chen, Y. / Khosla, D. | British Library Online Contents | 2015


    Space Object Identification using Deep Neural Networks

    McQuaid, Ian | British Library Conference Proceedings | 2018


    Driving Behaviors Recognition Using Deep Neural Networks

    Darwish, Karam / Ali, Majd | DataCite | 2023

    Freier Zugriff

    Space object classification using deep neural networks

    Jia, Bin / Pham, Khanh D. / Blasch, Erik et al. | IEEE | 2018