The increased use of Unmanned Aerial Vehicles (UAVs) in a variety of applications, including illegal activities such as drug trafficking and terrorism, has raised concerns about misuse. To protect special areas against illicit drones operations, UAVs' reconnaissance systems offer a promising solution. Having similar objects in the sky, like birds and airplanes, makes it difficult to detect these UAVs. In this work, a vision-based object recognition system, that uses a deep learning network called You Only Look Once version 5 (YOLOv5), is proposed to recognize drones and distinguish them from birds. The trained model is assessed using a set of performance measures such as mean Average Precision (mAP), precision, recall, F1-score and drone's location detection. A comparison with the YOLOv4 tool is carried out to show the effectiveness and superiority of the proposed YOLOv5 network.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vision-Based Drone Recognition Using a YOLOv5-like Deep Learning Network


    Beteiligte:


    Erscheinungsdatum :

    12.10.2023


    Format / Umfang :

    492753 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

    Yuliang Zhao / Zhongjie Ju / Tianang Sun et al. | DOAJ | 2023

    Freier Zugriff


    DroneRanger: Vision-Driven Deep Learning for Drone Distance Estimation

    Azad, Hamid / Mehta, Varun / Mantegh, Iraj et al. | IEEE | 2024


    Traffic sign recognition based on YOLOv5

    Hou, Fujin / huo, Yanqiang / Lu, Youfu et al. | SPIE | 2022


    Apple Recognition Algorithm Based on YOLOv5

    Xu, Wenzhe / Yao, Ziqian / Zhou, Xuelin et al. | IEEE | 2024