#7569. Leveraging single-shot detection and random sample consensus for wind turbine blade inspection

October 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Computational Mechanics;
Engineering (miscellaneous);
Mechanical Engineering;
Artificial Intelligence;
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Abstract:
Wind turbines require periodic inspection to ensure efficient power generation and a prolonged lifetime. Traditionally, inspection involves the risk of a person falling while abseiling from the top of the nacelle. To avoid this, drones have been controlled by operators to inspect the blades. However, this task requires expert pilots, who experience fatigue quickly. Alternatively, autonomous drones are not subject to human tiredness and can follow trajectories in a repeatable manner. Motivated by the latter, we introduce a vision-based blade detector capable of recognizing their orientation and relative position to generate a flight plan that allows it to safely collect image data.
Keywords:
Convolutional neural network; RANSAC; Simultaneous localization and mapping; Single-shot detector; Unmanned aerial vehicle; Wind turbine detection

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