Unmanned aerial vehicles (UAVs) have become indispensable in surveillance and security, particularly in intruder detection across diverse environments. Ornithopter UAVs that mimic bird movements are known for their exceptional versatility and noise reduction. This makes sure intelligence gathering happens without compromising people's personal space. The currently used surveillance system with stationary cameras and ground-based systems lacks wide coverage which hampers the system's effectiveness, especially in monitoring areas that are extensive or complicated terrains. The architecture is a hybrid system of ornithopter UAVs with the ESP32 microcontroller, OV2640 camera module, and YOLOv5 object detection algorithm to overcome these drawbacks. Deploying this innovative strategy maximizes intruder detectability by utilizing high-tech object recognition and tracking systems while retaining the plainness of pigeon-style flight. The incorporation represents an important leap in UAV-based monitoring, having the potential for a deep change in security, conservation, and disaster response. This overall solution has more intelligent surveillance which can perform many security and monitoring tasks in a better way, with increased precision and efficiency.


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    Title :

    YOLOv5-Enhanced Ornithopter UAV Simulator for Multi-object Detection


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:

    Conference:

    International Conference on Power Engineering and Intelligent Systems (PEIS) ; 2024 ; Srinagar, India March 16, 2024 - March 17, 2024



    Publication date :

    2024-12-05


    Size :

    18 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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