Mr. Daniel Langenkämper Profile

Daniel Langenkämper

at Univ Bielefeld

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Proceedings Article | 7 October 2019 Paper

Wind turbine segmentation performing kNN-clustering on superpixel segmentations

Torben Möller, Daniel Langenkämper, Tim Nattkemper

Proceedings Volume 11155, 1115524 (2019) https://doi.org/10.1117/12.2532764

KEYWORDS: Image segmentation, Wind turbine technology, Unmanned aerial vehicles, Inspection, Optical inspection, Video processing, Video

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A growing number of offshore wind farms have been deployed in recent years, comprising up to hundreds of wind turbines. The high number of wind turbines and the effort to reach an offshore wind turbine motivate the use of unmanned aerial vehicles (UAV) or drones for remote visual inspection. The images taken by the drones need to be inspected to detect and classify various kinds of damages. To leverage the effort for the detection and classification of damages, it is a good practice to segment the wind turbines from the background in a first step. In this paper, we compare the speed and segmentation accuracy of a k-Nearest-Neighbor classifier when applied to pixels or superpixels. Pixel-by-pixel classification is compared to superpixel classification using two different kinds of superpixel representations: Superpixels gained by Single Linear Iterative Clustering (SLIC) and Boundary Aware Superpixel Segmentation (BASS). Our results clearly show that superpixel based segmentation increases the segmentation speed by a relevant factor while the segmentation accuracy is at least on the same level or even improves in one dataset.

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