Search and rescue operations are critical tasks where quick and accurate detection of humans can save lives. Due to growing number of people doing adventure tourism and going to other inaccessible places it is very important to organize a search and rescue operations to aid and health care to the needy. It's very difficult to find people who are relatively very small considering the area where they are. The detection of persons and objects in images and videos captured by drones during operations is of great importance, and thus, the ability to do so automatically is highly significant. Due to their ability to quickly capture a large, controlled area in a short amount of time, UAVs or drones are becoming increasingly involved in search operations. Despite the high success rate in detecting objects in normal ground captured images, detecting humans or other objects from UAVs (Unmanned Aerial Vehicles) still remains a challenging task in captured images or aerial images due to a few challenges like weather conditions, varying attitude, pose and scale variations, artefacts like people wearing hats and camouflaged environment. This project use CNN detectors for human detection in search and rescue operations.


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    Titel :

    Automatic Person Detection in Search and Rescue Operations Using Deep Based MultiModel CNN Detectors


    Beteiligte:
    Vasudevan, I. (Autor:in) / Nithva, N.S. (Autor:in)


    Erscheinungsdatum :

    2023-07-29


    Format / Umfang :

    1684153 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch