Semi-supervised Surface Anomaly Detection of Composite Wind Turbine Blades from Drone Imagery

Jack Barker, Neelanjan Bhowmik, Toby Breckon, Toby Breckon

2022

Abstract

Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace. Turbine blades are susceptible to both operational and weather-based damage over time, reducing the energy efficiency output of turbines. In this study, we address automating the otherwise time-consuming task of both blade detection and extraction, together with fault detection within UAV-captured turbine blade inspection imagery. We propose BladeNet, an application-based, robust dual architecture to perform both unsupervised turbine blade detection and extraction, followed by super-pixel generation using the Simple Linear Iterative Clustering (SLIC) method to produce regional clusters. These clusters are then processed by a suite of semi-supervised detection methods. Our dual architecture detects surface faults of glass fibre composite material blades with high aptitude while requiring minimal prior manual image annotation. BladeNet produces an Average Precision (AP) of 0.995 across our Ørsted blade inspection dataset for offshore wind turbines and 0.223 across the Danish Technical University (DTU) NordTank turbine blade inspection dataset. BladeNet also obtains an AUC of 0.639 for surface anomaly detection across the Ørsted blade inspection dataset.

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Paper Citation


in Harvard Style

Barker J., Bhowmik N. and Breckon T. (2022). Semi-supervised Surface Anomaly Detection of Composite Wind Turbine Blades from Drone Imagery. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 868-876. DOI: 10.5220/0010842100003124


in Bibtex Style

@conference{visapp22,
author={Jack Barker and Neelanjan Bhowmik and Toby Breckon},
title={Semi-supervised Surface Anomaly Detection of Composite Wind Turbine Blades from Drone Imagery},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={868-876},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010842100003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Semi-supervised Surface Anomaly Detection of Composite Wind Turbine Blades from Drone Imagery
SN - 978-989-758-555-5
AU - Barker J.
AU - Bhowmik N.
AU - Breckon T.
PY - 2022
SP - 868
EP - 876
DO - 10.5220/0010842100003124