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Detecting anomalies in wind turbine blades from aerial images taken by drones can reduce the costs of periodic inspections. Deep learning is useful for image recognition, but it requires large amounts of data to be collected on rare abnormalities. In this paper, we propose a method to distinguish normal and abnormal parts of a blade by combining one-class support vector machine, an unsupervised learning...
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