Unsupervised anomaly detection with compact deep features for wind turbine blade images taken by a drone

Abstract

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 method, with deep features learned from a generic image dataset. The images taken by a drone are subsampled, projected to the feature space, and compressed by using principle component analysis (PCA) to make them learnable. Experiments show that features in the lower layers of deep nets are useful for detecting anomalies in blade images.