Evaluation of Propeller Inspection Using Different Deployment Strategies
Abstract: 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. https://www.bibliothek.tu-chemnitz.de/ojs/index.php/cs/article/view/661
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Evaluation of Propeller Inspection Using Different Deployment Strategies ; volume:10 ; number:8 ; day:23 ; month:12 ; year:2023
Embedded selforganising systems ; 10, Heft 8 (23.12.2023)
- Urheber
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Ikmel, Ghita
Harras, Mohamed Salim
Hardt, Wolfram
El Amrani El Idrissi, Najiba
- DOI
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10.14464/ess.v10i8.661
- URN
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urn:nbn:de:101:1-2024030618441479855012
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:54 MESZ
Datenpartner
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Beteiligte
- Ikmel, Ghita
- Harras, Mohamed Salim
- Hardt, Wolfram
- El Amrani El Idrissi, Najiba
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