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
Evaluation of Propeller Inspection Using Different Deployment Strategies
2023
18-24 Pages
Embedded Selforganising Systems, Vol. 10 No. 8 (2023): AI-driven Solutions for Sustainable Environment Monitoring
Sonstige
Elektronische Ressource
Englisch
PROPELLER UNIT DEPLOYMENT-TYPE UNMANNED AERIAL VEHICLE
Europäisches Patentamt | 2016
|Acoustic-based MAV propeller inspection
SLUB | 2025
|Inspection and servicing of aeromatic propeller
Engineering Index Backfile | 1945