In recent years, the use of Unmanned Aerial Vehicles (UAVs) for various applications has increased significantly. Among these applications, the inspection of infrastructures using UAVs has become a prominent area of research. This paper evaluates the efficiency of the YOLOv5 algorithm for propeller inspection. The algorithm's deployment across various platforms such as PC, Google Colab, and Jetson Nano is examined, with a focus on different deployment formats like PyTorch, ONNX, TensorFlow Lite, and others. The study highlights the often-overlooked importance of the deployment phase in the development of AI models and underscores its significance for the practical application of AI in real-world scenarios. Keywords— Computer vision, algorithm deployment, propeller inspection, Deployment strategies, efficiency improvement


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Evaluation of Propeller Inspection Using Different Deployment Strategies


    Beteiligte:

    Erscheinungsdatum :

    2023


    Format / Umfang :

    18-24 Pages


    Anmerkungen:

    Embedded Selforganising Systems, Vol. 10 No. 8 (2023): AI-driven Solutions for Sustainable Environment Monitoring



    Medientyp :

    Sonstige


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    PROPELLER UNIT DEPLOYMENT-TYPE UNMANNED AERIAL VEHICLE

    LEE SEON HO | Europäisches Patentamt | 2016

    Freier Zugriff

    Acoustic-based MAV propeller inspection

    Khadivi, Zahra / Technische Universität Chemnitz | SLUB | 2025


    Vision-based Propeller Damage Inspection Using Machine Learning

    Harras, Mohamed Salim / Saleh, Shadi / Battseren, Batbayar et al. | DataCite | 2023

    Freier Zugriff

    Inspection and servicing of aeromatic propeller

    Engineering Index Backfile | 1945


    Image classification for drone propeller inspection using deep learning

    Rahman, S. M. Rizwanur / Technische Universität Chemnitz | SLUB | 2024