Unmanned aerial vehicles (UAVs), or drones, have become indispensable in surveillance, search and rescue, and border security operations due to their versatility and ability to navigate challenging environments. Despite advancements in drone technology, real-time human detection remains a significant challenge, constrained by computational limitations, environmental variability, and the demand for high-speed processing. This paper introduces an innovative human detection system powered by the lightweight YOLOv3-tiny deep learning algorithm, integrated into a UAV platform for efficient and accurate detection. The system balances real-time performance with computational efficiency, making it well-suited for resource-limited environments. Extensive experiments validate its reliability across diverse scenarios, including military zone monitoring and border patrol operations. The results demonstrate its potential to address key challenges in UAV applications, offering a scalable and effective solution for enhancing autonomous security and surveillance systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    YOLO-DRONE: Deep Learning Deployment for Drone Human Detection


    Contributors:
    Alqahtani, A. (author) / Aljoufi, S. (author)


    Publication date :

    2025-04-13


    Size :

    1149756 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Performance Comparison of YOLO Algorithms in Drone Detection

    Jain, Rakshit / Shrivastav, Sujal / Kakde, Sumit et al. | IEEE | 2024


    Drone Deployment System

    RETIG ALAN / KREHER DAVID J | European Patent Office | 2017

    Free access

    Drone deployment system

    RETIG ALAN / KREHER DAVID J | European Patent Office | 2018

    Free access

    Drone Detection using YOLO and SSD A Comparative Study

    Pansare, Atharva / Sabu, Nidhi / Kushwaha, Himanshi et al. | IEEE | 2022