In this proposed work, UAV Thermal Imagery technology, which was employed by earlier researchers, to detect the Real-Time Victims, was taken. Due to their great durability, low cost, ease of implementation, and versatility, unmanned aerial vehicles (UAVs) have experienced a considerable surge in use. UAVs can swiftly search the impacted region during a natural catastrophe like earthquakes, floods, etc. to help more survival. One of the main factors to develop a rescue system is Dataset. In the various research papers studied the respective researchers used different algorithms or methods to develop a dataset. Researchers used methods like Reinforcement Learning, Deep Learning, YOLO Algorithms, etc. A comparative analysis of the various techniques was made. Also, the description of the Knowledge distillation process, which was discovered by earlier researchers, is mentioned here. Observation realises that Deep Learning methods are better to design a UAV.


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    Titel :

    Real-Time Survivor Detection in UAV Thermal Imagery Based on Deep Learning


    Beteiligte:
    Zade, Sharayu (Autor:in) / Tidke, Vedanti (Autor:in) / Gawande, Subodhi (Autor:in) / Dhule, Chetan (Autor:in) / Agrawal, Rahul (Autor:in) / Morris, Nekita Chavhan (Autor:in)


    Erscheinungsdatum :

    2023-10-06


    Format / Umfang :

    1847822 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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