This paper investigates the problem of aerial vehicle recognition using a text-guided deep convolutional neural network classifier. The network receives an aerial image and a desired class, and makes a yes or no output by matching the image and the textual description of the desired class. We train and test our model on a synthetic aerial dataset and our desired classes consist of the combination of the class types and colors of the vehicles. This strategy helps when considering more classes in testing than in training.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Convolutional Neural Networks for Aerial Vehicle Detection and Recognition


    Beteiligte:
    Soleimani, Amir (Autor:in) / Nasrabadi, Nasser M. (Autor:in) / Griffith, Elias (Autor:in) / Ralph, Jason (Autor:in) / Maskell, Simon (Autor:in)


    Erscheinungsdatum :

    2018-07-01


    Format / Umfang :

    6803197 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Lightweight Convolutional Neural Networks for Vehicle Target Recognition

    Wang, Jintao / Ji, Ping / Xiao, Wen et al. | IEEE | 2020


    Automated Vehicle Recognition with Deep Convolutional Neural Networks

    Adu-Gyamfi, Yaw Okyere / Asare, Sampson Kwasi / Sharma, Anuj et al. | Transportation Research Record | 2017


    Convolutional neural networks on small unmanned aerial systems

    Kaster, Joshua / Patrick, James / Clouse, Hamilton Scott | IEEE | 2017


    Wildfire Classification Using Infrared Unmanned Aerial Vehicle Data with Convolutional Neural Networks

    Aral, Rahmi Arda / Zalluhoglu, Cemil / Sezer, Ebru Akcapinar | IEEE | 2023