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

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. The proposed blade detector extracts line features with the camera, which are filtered to reduce the search space by using bounding boxes. They are obtained with a single-shot detector based on a convolutional neural network. Finally, a random sample consensus procedure finds the lines that best fit a geometrical model of the wind turbine. We compare our deep learning approach against a color segmentation method, showing that it is up to 6 times faster. We also compare against guided search during random sampling, which exploits the separate boxes detected by the network, seeking to reduce the number of outliers. We conclude with an illustrative example of how our proposed detector could be used for autonomous wind turbine inspection.

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Acknowledgements

This research project has been funded by Conacyt grant 291137 and Conacyt-INEGI project 268528.

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Correspondence to Jose Martinez-Carranza.

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Parlange, R., Martinez-Carranza, J. Leveraging single-shot detection and random sample consensus for wind turbine blade inspection. Intel Serv Robotics 14, 611–628 (2021). https://doi.org/10.1007/s11370-021-00383-6

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Keywords

  • Wind turbine detection
  • Unmanned aerial vehicle
  • RANSAC
  • Single-shot detector
  • Convolutional neural network
  • Simultaneous localization and mapping